1_exploratory_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Data was obtained from Scopus using the following search query:\n",
    "\n",
    "REF ( \"the nature of belief systems in mass publics\" ) AND ( LIMIT-TO ( LANGUAGE , \"English\" ) )\n",
    "\n",
    "and \n",
    "\n",
    "REF ( \"structure of decision\" AND axelrod ) AND ( LIMIT-TO ( LANGUAGE , \"English\" ) )\n",
    "\n",
    "and\n",
    "\n",
    "REF ( \"Women, fire, and dangerous things: What categories reveal about the mind\" )\n",
    "\n",
    "\n",
    "All the results were exported to a CSV file, scopus.csv, and then imported into a Pandas DataFrame.\n",
    "'''\n",
    "\n",
    "\n",
    "nature_of_belief = pd.read_csv('data/seeds/nature_of_belief.csv').assign(seed_paper = 'nature_of_belief')\n",
    "structure_of_decision = pd.read_csv('data/seeds/structure_of_decision.csv').assign(seed_paper = 'structure_of_decision')\n",
    "women_fire_dangerous = pd.read_csv('data/seeds/women_fire_dangerous.csv').assign(seed_paper = 'women_fire_dangerous')\n",
    "\n",
    "papers = pd.concat([nature_of_belief, structure_of_decision, women_fire_dangerous], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Keywords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TITLE-ABS-KEY (\"Belief* Network\" OR \"Belief* System\" OR \"Belief* Map\" OR \"Belief* Set\" OR \"Issue* Network\" OR \"Issue* System\" OR \"Issue* Map\" OR \"Issue* Set\" OR \"Opinion* Network\" OR \"Opinion* System\" OR \"Opinion* Map\" OR \"Opinion* Set\" OR \"Value System\" OR \"Value Set\" OR \"Attitude* Network\" OR \"Attitude* System\" OR \"Attitude* Map\" OR \"Attitude* Set\" OR \"Cogni* System\" OR \"Cogni* Map\" OR \"Cogni* Set\" OR \"Network of Belief\" OR \"Network of Issue\" OR \"Network of Opinion\" OR \"Network of Attitudes\" OR \"Network of Cogni*\" OR \"System* of Beliefs\" OR \"System* of Issue\" OR \"System *of Opinion\" OR \"System* of Value\" OR \"System* of Attitude\" OR \"Map* of Belief\" OR \"Map* of Issue\" OR \"Map* of Opinion\" OR \"Map* of Attitude\" OR \"Relationship Between Belief\" OR \"Relationship Between Opinion\" OR \"Relationship Between Value\" OR \"Relationship Between Attitude\" OR \"Mental Map\")\n"
     ]
    }
   ],
   "source": [
    "keywords = [\n",
    "    # 'network',\n",
    "    'network of belief',\n",
    "    'networks of belief',\n",
    "    'belief network',\n",
    "    'beliefs network',\n",
    "    'map of belief',\n",
    "    'maps of belief',\n",
    "    'relationship between belief',\n",
    "    'cognitive map',\n",
    "    'map of cognitive behavior',\n",
    "    'map of opinion',\n",
    "    'network of opinion',\n",
    "    'networks of opinion',\n",
    "    'relationship between opinion',\n",
    "    'system of belief',\n",
    "    'systems of belief',\n",
    "    'belief system',\n",
    "    'beliefs system',\n",
    "    'set of opinion',\n",
    "    'opinion set',\n",
    "    'set of value',\n",
    "    'values set',\n",
    "    'system of opinion',\n",
    "    'opinion system', \n",
    "    'opimions system',\n",
    "    'system of value',\n",
    "    'values system',\n",
    "    'value system',\n",
    "    'attitude network',\n",
    "    'attitude map',\n",
    "    'relationship between attitude',\n",
    "    'attitude system',\n",
    "    # 'attitude',\n",
    "    'issue system'\n",
    "    'factor system',\n",
    "    'system of factor',\n",
    "    'variable system',\n",
    "    'system of variable',\n",
    "    'crucial factor',\n",
    "    # additional ones after 2023.11.20\n",
    "    'correlational class analysis',\n",
    "    'relational class analysis',\n",
    "    'ideology system',\n",
    "    'map of idea',\n",
    "    'system of idea',\n",
    "    # use things like * in scopus: \"behav* network\" would match behavior network and behavioural network both; from https://library.bath.ac.uk/scopus/keyword-searching\n",
    "]\n",
    "\n",
    "\n",
    "systematic_keywords = [\n",
    "    'Belief* Network',\n",
    "    'Belief* System',\n",
    "    'Belief* Map',\n",
    "    'Belief* Set',\n",
    "    'Issue* Network',\n",
    "    'Issue* System',\n",
    "    'Issue* Map',\n",
    "    'Issue* Set',\n",
    "    'Opinion* Network',\n",
    "    'Opinion* System', # keep an eye on this one\n",
    "    'Opinion* Map',\n",
    "    'Opinion* Set',\n",
    "    # 'Value Network', catches valued networks too, physics heavy application\n",
    "    # 'Values Network',\n",
    "    'Value System',\n",
    "    # 'Value* Map', business heavy application\n",
    "    'Value Set',\n",
    "    'Attitude* Network',\n",
    "    'Attitude* System',\n",
    "    'Attitude* Map',\n",
    "    'Attitude* Set',\n",
    "    # 'Cogni* Network', catches engineering and medical applications\n",
    "    'Cogni* System',\n",
    "    'Cogni* Map', # cognitive catches a lot of things, FUZZY exclude needed\n",
    "    'Cogni* Set', \n",
    "    'Network of Belief',\n",
    "    'Network of Issue',\n",
    "    'Network of Opinion',\n",
    "    # 'Network of Values', catching SBM stuff\n",
    "    'Network of Attitudes',\n",
    "    'Network of Cogni*', # FINISHED HERE 2023.11.20\n",
    "    'System* of Beliefs',\n",
    "    'System* of Issue',\n",
    "    'System *of Opinion',\n",
    "    'System* of Value',\n",
    "    'System* of Attitude',\n",
    "    # 'System* of Cogni*', catches neuroscience and dual process stuff\n",
    "    'Map* of Belief',\n",
    "    'Map* of Issue',\n",
    "    'Map* of Opinion',\n",
    "    # 'Map of Values',\n",
    "    'Map* of Attitude',\n",
    "    # 'Map of Cognitives', neuroscience heavy application\n",
    "    # 'Set of Beliefs',\n",
    "    # 'Set of Issues',\n",
    "    # 'Set of Opinions',\n",
    "    # 'Set of Values',\n",
    "    # 'Set of Attitudes',\n",
    "    # 'Set of Cognitives', lot of noise in all the \"set\" keywords\n",
    "    'Relationship Between Belief',\n",
    "    # 'Relationship Between of Issue' , ?\n",
    "    'Relationship Between Opinion',\n",
    "    'Relationship Between Value',\n",
    "    'Relationship Between Attitude',\n",
    "    # 'Relationship Between of Cognitives',\n",
    "    \"Mental Map\",\n",
    "]\n",
    "\n",
    "\n",
    "joker_keywords = [\n",
    "    \n",
    "]\n",
    "\n",
    "\n",
    "def generate_query(keywords):\n",
    "    # Joining keywords with ' OR ' and enclosing each in quotation marks\n",
    "    joined_keywords = ' OR '.join(f'\"{keyword}\"' for keyword in keywords)\n",
    "    # Constructing the final query\n",
    "    query = f'TITLE-ABS-KEY ({joined_keywords})'\n",
    "    return query\n",
    "\n",
    "query = generate_query(systematic_keywords)\n",
    "print(query)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtering on keywords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Authors</th>\n",
       "      <th>Author full names</th>\n",
       "      <th>Author(s) ID</th>\n",
       "      <th>Title</th>\n",
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       "      <th>Art. No.</th>\n",
       "      <th>Page start</th>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Enders A.; Klofstad C.; Stoler J.; Uscinski J.E.</td>\n",
       "      <td>Enders, Adam (57200699056); Klofstad, Casey (1...</td>\n",
       "      <td>57200699056; 10340526300; 26533535500; 2417218...</td>\n",
       "      <td>How Anti-Social Personality Traits and Anti-Es...</td>\n",
       "      <td>2023</td>\n",
       "      <td>American Politics Research</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>247</td>\n",
       "      <td>...</td>\n",
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       "      <td>Conspiracy theories and misinformation (CTM) b...</td>\n",
       "      <td>conspiracy theory; COVID-19; dark triad; Donal...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>All Open Access; Green Open Access</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-85142176957</td>\n",
       "      <td>nature_of_belief</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tilley J.; Hobolt S.B.</td>\n",
       "      <td>Tilley, James (7007162303); Hobolt, Sara B. (9...</td>\n",
       "      <td>7007162303; 9634821200</td>\n",
       "      <td>Brexit as an Identity: Political Identities an...</td>\n",
       "      <td>2023</td>\n",
       "      <td>PS - Political Science and Politics</td>\n",
       "      <td>56</td>\n",
       "      <td>4</td>\n",
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       "      <td>546</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.scopus.com/inward/record.uri?eid=2...</td>\n",
       "      <td>The decision by a narrow majority of British v...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Conference paper</td>\n",
       "      <td>Final</td>\n",
       "      <td>All Open Access; Green Open Access; Hybrid Gol...</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-85168668039</td>\n",
       "      <td>nature_of_belief</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Groenendyk E.; Kimbrough E.O.; Pickup M.</td>\n",
       "      <td>Groenendyk, Eric (6506537141); Kimbrough, Erik...</td>\n",
       "      <td>6506537141; 35484426400; 15757997300</td>\n",
       "      <td>How Norms Shape the Nature of Belief Systems i...</td>\n",
       "      <td>2023</td>\n",
       "      <td>American Journal of Political Science</td>\n",
       "      <td>67</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>623</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.scopus.com/inward/record.uri?eid=2...</td>\n",
       "      <td>How should ideology be understood, and should ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-85134518364</td>\n",
       "      <td>nature_of_belief</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Calice M.N.; Bao L.; Freiling I.; Howell E.; X...</td>\n",
       "      <td>Calice, Mikhaila N. (57226542498); Bao, Luye (...</td>\n",
       "      <td>57226542498; 57219894725; 57197771888; 5719384...</td>\n",
       "      <td>Polarized platforms? How partisanship shapes p...</td>\n",
       "      <td>2023</td>\n",
       "      <td>New Media and Society</td>\n",
       "      <td>25</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2833</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.scopus.com/inward/record.uri?eid=2...</td>\n",
       "      <td>The use of artificial intelligence-based algor...</td>\n",
       "      <td>Algorithms; artificial intelligence; gatekeepi...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>All Open Access; Hybrid Gold Open Access</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-85111852187</td>\n",
       "      <td>nature_of_belief</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Mongrain P.</td>\n",
       "      <td>Mongrain, Philippe (57202605696)</td>\n",
       "      <td>57202605696</td>\n",
       "      <td>With a little help from my friends? The impact...</td>\n",
       "      <td>2023</td>\n",
       "      <td>European Journal of Political Research</td>\n",
       "      <td>62</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1320</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.scopus.com/inward/record.uri?eid=2...</td>\n",
       "      <td>Partisan preferences usually stand out as the ...</td>\n",
       "      <td>citizen forecasting; contact model; expectatio...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-85144013284</td>\n",
       "      <td>nature_of_belief</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12628</th>\n",
       "      <td>Strong G.W.; Whitehead B.A.</td>\n",
       "      <td>Strong, Gary W. (35966959200); Whitehead, Bruc...</td>\n",
       "      <td>35966959200; 7005200466</td>\n",
       "      <td>A solution to the tag-assignment problem for n...</td>\n",
       "      <td>1989</td>\n",
       "      <td>Behavioral and Brain Sciences</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>381</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.scopus.com/inward/record.uri?eid=2...</td>\n",
       "      <td>Purely parallel neural networks can model obje...</td>\n",
       "      <td>affordance; attention; connectionist network; ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-84971765284</td>\n",
       "      <td>women_fire_dangerous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12635</th>\n",
       "      <td>Plaut D.C.</td>\n",
       "      <td>Plaut, David C (57207497092)</td>\n",
       "      <td>57207497092</td>\n",
       "      <td>Neural networks and computational theory: Solv...</td>\n",
       "      <td>1989</td>\n",
       "      <td>Behavioral and Brain Sciences</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>411</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.scopus.com/inward/record.uri?eid=2...</td>\n",
       "      <td>[No abstract available]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-84971735697</td>\n",
       "      <td>women_fire_dangerous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12643</th>\n",
       "      <td>Kruschke J.K.</td>\n",
       "      <td>Kruschke, John K. (7004103690)</td>\n",
       "      <td>7004103690</td>\n",
       "      <td>State transitions in constraint satisfaction n...</td>\n",
       "      <td>1989</td>\n",
       "      <td>Behavioral and Brain Sciences</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>407</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.scopus.com/inward/record.uri?eid=2...</td>\n",
       "      <td>[No abstract available]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-84971791514</td>\n",
       "      <td>women_fire_dangerous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12694</th>\n",
       "      <td>Boyce W.T.; Kay M.; Uitti C.</td>\n",
       "      <td>Boyce, W.Thomas (56432345900); Kay, Margarita ...</td>\n",
       "      <td>56432345900; 7202188488; 7801372570</td>\n",
       "      <td>The taxonomy of social support: An ethnographi...</td>\n",
       "      <td>1988</td>\n",
       "      <td>Social Science and Medicine</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1079</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.scopus.com/inward/record.uri?eid=2...</td>\n",
       "      <td>Current approaches to assessing the health-rel...</td>\n",
       "      <td>adolescent mothers; social networks; social su...</td>\n",
       "      <td>Adolescent; Classification; Cognition; Compara...</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-0023806248</td>\n",
       "      <td>women_fire_dangerous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12709</th>\n",
       "      <td>Smolensky P.</td>\n",
       "      <td>Smolensky, Paul (6602511111)</td>\n",
       "      <td>6602511111</td>\n",
       "      <td>On the proper treatment of connectionism</td>\n",
       "      <td>1988</td>\n",
       "      <td>Behavioral and Brain Sciences</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.scopus.com/inward/record.uri?eid=2...</td>\n",
       "      <td>A set of hypotheses is formulated for a connec...</td>\n",
       "      <td>cognition; computation; connectionism; dynamic...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-84971179405</td>\n",
       "      <td>women_fire_dangerous</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2955 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Authors  \\\n",
       "1       Enders A.; Klofstad C.; Stoler J.; Uscinski J.E.   \n",
       "3                                 Tilley J.; Hobolt S.B.   \n",
       "4               Groenendyk E.; Kimbrough E.O.; Pickup M.   \n",
       "6      Calice M.N.; Bao L.; Freiling I.; Howell E.; X...   \n",
       "11                                           Mongrain P.   \n",
       "...                                                  ...   \n",
       "12628                        Strong G.W.; Whitehead B.A.   \n",
       "12635                                         Plaut D.C.   \n",
       "12643                                      Kruschke J.K.   \n",
       "12694                       Boyce W.T.; Kay M.; Uitti C.   \n",
       "12709                                       Smolensky P.   \n",
       "\n",
       "                                       Author full names  \\\n",
       "1      Enders, Adam (57200699056); Klofstad, Casey (1...   \n",
       "3      Tilley, James (7007162303); Hobolt, Sara B. (9...   \n",
       "4      Groenendyk, Eric (6506537141); Kimbrough, Erik...   \n",
       "6      Calice, Mikhaila N. (57226542498); Bao, Luye (...   \n",
       "11                      Mongrain, Philippe (57202605696)   \n",
       "...                                                  ...   \n",
       "12628  Strong, Gary W. (35966959200); Whitehead, Bruc...   \n",
       "12635                       Plaut, David C (57207497092)   \n",
       "12643                     Kruschke, John K. (7004103690)   \n",
       "12694  Boyce, W.Thomas (56432345900); Kay, Margarita ...   \n",
       "12709                       Smolensky, Paul (6602511111)   \n",
       "\n",
       "                                            Author(s) ID  \\\n",
       "1      57200699056; 10340526300; 26533535500; 2417218...   \n",
       "3                                 7007162303; 9634821200   \n",
       "4                   6506537141; 35484426400; 15757997300   \n",
       "6      57226542498; 57219894725; 57197771888; 5719384...   \n",
       "11                                           57202605696   \n",
       "...                                                  ...   \n",
       "12628                            35966959200; 7005200466   \n",
       "12635                                        57207497092   \n",
       "12643                                         7004103690   \n",
       "12694                56432345900; 7202188488; 7801372570   \n",
       "12709                                         6602511111   \n",
       "\n",
       "                                                   Title  Year  \\\n",
       "1      How Anti-Social Personality Traits and Anti-Es...  2023   \n",
       "3      Brexit as an Identity: Political Identities an...  2023   \n",
       "4      How Norms Shape the Nature of Belief Systems i...  2023   \n",
       "6      Polarized platforms? How partisanship shapes p...  2023   \n",
       "11     With a little help from my friends? The impact...  2023   \n",
       "...                                                  ...   ...   \n",
       "12628  A solution to the tag-assignment problem for n...  1989   \n",
       "12635  Neural networks and computational theory: Solv...  1989   \n",
       "12643  State transitions in constraint satisfaction n...  1989   \n",
       "12694  The taxonomy of social support: An ethnographi...  1988   \n",
       "12709           On the proper treatment of connectionism  1988   \n",
       "\n",
       "                                 Source title Volume Issue Art. No.  \\\n",
       "1                  American Politics Research     51     2      NaN   \n",
       "3         PS - Political Science and Politics     56     4      NaN   \n",
       "4       American Journal of Political Science     67     3      NaN   \n",
       "6                       New Media and Society     25    11      NaN   \n",
       "11     European Journal of Political Research     62     4      NaN   \n",
       "...                                       ...    ...   ...      ...   \n",
       "12628           Behavioral and Brain Sciences     12     3      NaN   \n",
       "12635           Behavioral and Brain Sciences     12     3      NaN   \n",
       "12643           Behavioral and Brain Sciences     12     3      NaN   \n",
       "12694             Social Science and Medicine     26    11      NaN   \n",
       "12709           Behavioral and Brain Sciences     11     1      NaN   \n",
       "\n",
       "      Page start  ...                                               Link  \\\n",
       "1            247  ...  https://www.scopus.com/inward/record.uri?eid=2...   \n",
       "3            546  ...  https://www.scopus.com/inward/record.uri?eid=2...   \n",
       "4            623  ...  https://www.scopus.com/inward/record.uri?eid=2...   \n",
       "6           2833  ...  https://www.scopus.com/inward/record.uri?eid=2...   \n",
       "11          1320  ...  https://www.scopus.com/inward/record.uri?eid=2...   \n",
       "...          ...  ...                                                ...   \n",
       "12628        381  ...  https://www.scopus.com/inward/record.uri?eid=2...   \n",
       "12635        411  ...  https://www.scopus.com/inward/record.uri?eid=2...   \n",
       "12643        407  ...  https://www.scopus.com/inward/record.uri?eid=2...   \n",
       "12694       1079  ...  https://www.scopus.com/inward/record.uri?eid=2...   \n",
       "12709          1  ...  https://www.scopus.com/inward/record.uri?eid=2...   \n",
       "\n",
       "                                                Abstract  \\\n",
       "1      Conspiracy theories and misinformation (CTM) b...   \n",
       "3      The decision by a narrow majority of British v...   \n",
       "4      How should ideology be understood, and should ...   \n",
       "6      The use of artificial intelligence-based algor...   \n",
       "11     Partisan preferences usually stand out as the ...   \n",
       "...                                                  ...   \n",
       "12628  Purely parallel neural networks can model obje...   \n",
       "12635                            [No abstract available]   \n",
       "12643                            [No abstract available]   \n",
       "12694  Current approaches to assessing the health-rel...   \n",
       "12709  A set of hypotheses is formulated for a connec...   \n",
       "\n",
       "                                         Author Keywords  \\\n",
       "1      conspiracy theory; COVID-19; dark triad; Donal...   \n",
       "3                                                    NaN   \n",
       "4                                                    NaN   \n",
       "6      Algorithms; artificial intelligence; gatekeepi...   \n",
       "11     citizen forecasting; contact model; expectatio...   \n",
       "...                                                  ...   \n",
       "12628  affordance; attention; connectionist network; ...   \n",
       "12635                                                NaN   \n",
       "12643                                                NaN   \n",
       "12694  adolescent mothers; social networks; social su...   \n",
       "12709  cognition; computation; connectionism; dynamic...   \n",
       "\n",
       "                                          Index Keywords     Document Type  \\\n",
       "1                                                    NaN           Article   \n",
       "3                                                    NaN  Conference paper   \n",
       "4                                                    NaN           Article   \n",
       "6                                                    NaN           Article   \n",
       "11                                                   NaN           Article   \n",
       "...                                                  ...               ...   \n",
       "12628                                                NaN           Article   \n",
       "12635                                                NaN           Article   \n",
       "12643                                                NaN           Article   \n",
       "12694  Adolescent; Classification; Cognition; Compara...           Article   \n",
       "12709                                                NaN           Article   \n",
       "\n",
       "      Publication Stage                                        Open Access  \\\n",
       "1                 Final                 All Open Access; Green Open Access   \n",
       "3                 Final  All Open Access; Green Open Access; Hybrid Gol...   \n",
       "4                 Final                                                NaN   \n",
       "6                 Final           All Open Access; Hybrid Gold Open Access   \n",
       "11                Final                                                NaN   \n",
       "...                 ...                                                ...   \n",
       "12628             Final                                                NaN   \n",
       "12635             Final                                                NaN   \n",
       "12643             Final                                                NaN   \n",
       "12694             Final                                                NaN   \n",
       "12709             Final                                                NaN   \n",
       "\n",
       "       Source                 EID            seed_paper  \n",
       "1      Scopus  2-s2.0-85142176957      nature_of_belief  \n",
       "3      Scopus  2-s2.0-85168668039      nature_of_belief  \n",
       "4      Scopus  2-s2.0-85134518364      nature_of_belief  \n",
       "6      Scopus  2-s2.0-85111852187      nature_of_belief  \n",
       "11     Scopus  2-s2.0-85144013284      nature_of_belief  \n",
       "...       ...                 ...                   ...  \n",
       "12628  Scopus  2-s2.0-84971765284  women_fire_dangerous  \n",
       "12635  Scopus  2-s2.0-84971735697  women_fire_dangerous  \n",
       "12643  Scopus  2-s2.0-84971791514  women_fire_dangerous  \n",
       "12694  Scopus   2-s2.0-0023806248  women_fire_dangerous  \n",
       "12709  Scopus  2-s2.0-84971179405  women_fire_dangerous  \n",
       "\n",
       "[2955 rows x 24 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if the title or abstract contains any of the keywords\n",
    "\n",
    "def scopus_query_to_regex(query):\n",
    "    \"\"\"\n",
    "    Converts a Scopus-style query with wildcards into a regex pattern.\n",
    "\n",
    "    Args:\n",
    "    query (str): The Scopus-style query string.\n",
    "\n",
    "    Returns:\n",
    "    str: A regex pattern corresponding to the Scopus-style query.\n",
    "    \"\"\"\n",
    "    # Split the query into words\n",
    "    words = query.split()\n",
    "\n",
    "    # Replace '*' in each word with '\\S*' and add '\\s+' at the end of each word\n",
    "    regex_parts = [word.replace('*', '\\\\S*') + '\\\\s+' for word in words]\n",
    "\n",
    "    # Join the parts to form the final regex pattern\n",
    "    regex_pattern = ''.join(regex_parts)\n",
    "\n",
    "    return regex_pattern\n",
    "\n",
    "\n",
    "   \n",
    "\n",
    "\n",
    "def check_keywords(kw_list, text):\n",
    "    if isinstance(text, float):\n",
    "        return False\n",
    "    \n",
    "    for kw in kw_list:\n",
    "        if kw.lower() in text.lower():\n",
    "            return True\n",
    "    return False\n",
    "\n",
    "\n",
    "abstract = papers.Abstract.apply(lambda x: check_keywords(keywords, x))\n",
    "title = papers.Title.apply(lambda x: check_keywords(keywords, x))\n",
    "auth_kws = papers['Author Keywords'].apply(lambda x: check_keywords(keywords, x))\n",
    "\n",
    "# Combine the results\n",
    "papers[\n",
    "    abstract | \n",
    "    title | \n",
    "    auth_kws\n",
    "    ]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Keyword frequencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>seed_paper</th>\n",
       "      <th>nature_of_belief</th>\n",
       "      <th>structure_of_decision</th>\n",
       "      <th>women_fire_dangerous</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keyword</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>attitude</th>\n",
       "      <td>1617</td>\n",
       "      <td>30</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attitude network</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attitude system</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>belief network</th>\n",
       "      <td>14</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>belief system</th>\n",
       "      <td>233</td>\n",
       "      <td>42</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cognitive map</th>\n",
       "      <td>8</td>\n",
       "      <td>2378</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crucial factor</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>network</th>\n",
       "      <td>258</td>\n",
       "      <td>441</td>\n",
       "      <td>404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>opinion set</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relationship between attitude</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relationship between belief</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relationship between opinion</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>set of opinion</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>set of value</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>system of belief</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>system of factor</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>system of value</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>system of variable</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>systems of belief</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value system</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "seed_paper                     nature_of_belief  structure_of_decision  \\\n",
       "keyword                                                                  \n",
       "attitude                                   1617                     30   \n",
       "attitude network                              5                      0   \n",
       "attitude system                               2                      0   \n",
       "belief network                               14                     31   \n",
       "belief system                               233                     42   \n",
       "cognitive map                                 8                   2378   \n",
       "crucial factor                                3                      0   \n",
       "network                                     258                    441   \n",
       "opinion set                                   1                      0   \n",
       "relationship between attitude                 6                      0   \n",
       "relationship between belief                   1                      1   \n",
       "relationship between opinion                  1                      0   \n",
       "set of opinion                                2                      0   \n",
       "set of value                                  1                      2   \n",
       "system of belief                              1                      1   \n",
       "system of factor                              0                      1   \n",
       "system of value                               1                      0   \n",
       "system of variable                            0                      1   \n",
       "systems of belief                             2                      0   \n",
       "value system                                  3                      3   \n",
       "\n",
       "seed_paper                     women_fire_dangerous  \n",
       "keyword                                              \n",
       "attitude                                        114  \n",
       "attitude network                                  0  \n",
       "attitude system                                   0  \n",
       "belief network                                    0  \n",
       "belief system                                     7  \n",
       "cognitive map                                    13  \n",
       "crucial factor                                    2  \n",
       "network                                         404  \n",
       "opinion set                                       0  \n",
       "relationship between attitude                     0  \n",
       "relationship between belief                       0  \n",
       "relationship between opinion                      0  \n",
       "set of opinion                                    0  \n",
       "set of value                                      1  \n",
       "system of belief                                  0  \n",
       "system of factor                                  0  \n",
       "system of value                                   3  \n",
       "system of variable                                0  \n",
       "systems of belief                                 2  \n",
       "value system                                      4  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def keyword_frequency(seed_paper_category, keyword, columns=['Title', 'Abstract', 'Author Keywords']):\n",
    "    keyword_count = 0\n",
    "    subset_papers = papers[papers['seed_paper'] == seed_paper_category]\n",
    "    \n",
    "    for col in columns:\n",
    "        keyword_count += subset_papers[col].apply(lambda x: keyword.lower() in str(x).lower()).sum()\n",
    "        \n",
    "    return keyword_count\n",
    "\n",
    "# Creating a list to store the results\n",
    "result_df = []\n",
    "\n",
    "for seed_paper_category in papers['seed_paper'].unique():\n",
    "    for kw in keywords:\n",
    "        frequency = keyword_frequency(seed_paper_category, kw)\n",
    "        result_df.append({'seed_paper': seed_paper_category,\n",
    "                                      'keyword': kw,\n",
    "                                      'frequency': frequency})\n",
    "\n",
    "\n",
    "# Convert the list of dictionaries into a DataFrame\n",
    "result_df = pd.DataFrame(result_df)\n",
    "\n",
    "# Create a pivot table with 'keyword' as the index, 'seed_paper' as columns, and 'frequency' as values\n",
    "pivot_table = result_df.pivot_table(index='keyword', columns='seed_paper', values='frequency', aggfunc='sum')\n",
    "\n",
    "\n",
    "# Display the pivot table, where not all columns are zeros\n",
    "pivot_table.loc[(pivot_table!=0).any(axis=1)]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bocsar000/.pyenv/versions/3.10.13/envs/scopus_scrape/lib/python3.10/site-packages/sklearn/feature_extraction/text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                           avg_word2vec  \\\n",
      "0     [0.15551154, 0.4843057, -0.063843034, -0.12856...   \n",
      "1     [0.0017258335, 0.6096328, -0.068927474, -0.076...   \n",
      "2     [-0.20611556, 0.4250914, 0.32713714, -0.062033...   \n",
      "3     [-0.051825833, 0.43890238, 0.04432644, -0.0149...   \n",
      "4     [0.022352302, 0.6387013, 0.07324514, -0.117399...   \n",
      "...                                                 ...   \n",
      "6354  [-0.08334337, 0.3148329, 0.04779819, 0.0082024...   \n",
      "6355  [-0.18945162, 0.50257474, 0.4241818, -0.081269...   \n",
      "6356  [-0.109972484, 0.3742927, 0.11006194, -0.00462...   \n",
      "6357  [-0.5276574, 0.3469131, 0.18516265, -0.150177,...   \n",
      "6358  [-0.11751811, 0.33437216, 0.19116303, -0.04253...   \n",
      "\n",
      "                                tfidf_weighted_word2vec  \n",
      "0     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
      "1     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
      "2     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
      "3     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
      "4     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
      "...                                                 ...  \n",
      "6354  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
      "6355  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
      "6356  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
      "6357  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
      "6358  [-0.045079794, 0.50794685, 0.18370537, -0.0257...  \n",
      "\n",
      "[6359 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from gensim.models import Word2Vec\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from collections import defaultdict\n",
    "\n",
    "# Assuming 'papers' is your DataFrame and 'Abstracts' is the column with text data\n",
    "# Make sure to preprocess your text (e.g., lowercasing, removing punctuation)\n",
    "# Here's a simple tokenizer function that also removes stopwords\n",
    "def tokenize(text):\n",
    "    stop_words = set(stopwords.words('english'))\n",
    "    return [word for word in word_tokenize(text.lower()) if word.isalpha() and word not in stop_words]\n",
    "\n",
    "# Tokenize the abstracts\n",
    "papers['tokenized_abstracts'] = papers['Abstract'].apply(tokenize)\n",
    "\n",
    "# Train a Word2Vec model or load a pre-trained one\n",
    "# For the example, we're training a new model on the abstracts\n",
    "model = Word2Vec(sentences=papers['tokenized_abstracts'], vector_size=100, window=5, min_count=1, workers=4)\n",
    "\n",
    "# Define a function to average word vectors for a document\n",
    "def average_word_vectors(words, model, vocabulary, num_features):\n",
    "    feature_vector = np.zeros((num_features,), dtype=\"float32\")\n",
    "    nwords = 0.\n",
    "\n",
    "    for word in words:\n",
    "        if word in vocabulary: \n",
    "            nwords = nwords + 1.\n",
    "            feature_vector = np.add(feature_vector, model.wv[word])\n",
    "\n",
    "    if nwords:\n",
    "        feature_vector = np.divide(feature_vector, nwords)\n",
    "    return feature_vector\n",
    "\n",
    "\n",
    "# Get the Word2Vec model vocabulary\n",
    "vocab = set(model.wv.index_to_key)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>seed_paper</th>\n",
       "      <th>nature_of_belief</th>\n",
       "      <th>structure_of_decision</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keyword</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>belief system</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beliefs system</th>\n",
       "      <td>732</td>\n",
       "      <td>611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>opimions system</th>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>opinion set</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>opinion system</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>set of opinion</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>system of belief</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>system of opinion</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>system of value</th>\n",
       "      <td>205</td>\n",
       "      <td>1082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>systems of belief</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value system</th>\n",
       "      <td>205</td>\n",
       "      <td>1082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>values set</th>\n",
       "      <td>357</td>\n",
       "      <td>536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>values system</th>\n",
       "      <td>40</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "seed_paper         nature_of_belief  structure_of_decision\n",
       "keyword                                                   \n",
       "belief system                     6                      5\n",
       "beliefs system                  732                    611\n",
       "opimions system                   3                     27\n",
       "opinion set                      40                      1\n",
       "opinion system                    7                      0\n",
       "set of opinion                   40                      1\n",
       "system of belief                  6                      5\n",
       "system of opinion                 7                      0\n",
       "system of value                 205                   1082\n",
       "systems of belief                 1                      0\n",
       "value system                    205                   1082\n",
       "values set                      357                    536\n",
       "values system                    40                    156"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "def keyword_similarity(seed_paper_category, keyword_vector, columns=['Abstract'], threshold=0.5):\n",
    "    similarity_count = 0\n",
    "    subset_papers = papers[papers['seed_paper'] == seed_paper_category]\n",
    "    \n",
    "    # Calculate similarity for each paper in the subset\n",
    "    for index, paper in subset_papers.iterrows():\n",
    "        for col in columns:\n",
    "            # Tokenize the text in the column\n",
    "            text_tokens = tokenize(str(paper[col]))\n",
    "            # Skip empty columns to avoid errors\n",
    "            if not text_tokens:\n",
    "                continue\n",
    "            # Get the Word2Vec vector for each word in the column\n",
    "            col_vectors = [model.wv[word] for word in text_tokens if word in model.wv]\n",
    "            # If there are no vectors, skip this column\n",
    "            if not col_vectors:\n",
    "                continue\n",
    "            # Calculate average vector for the column\n",
    "            avg_col_vector = np.mean(col_vectors, axis=0).reshape(1, -1)\n",
    "            # Calculate cosine similarity between keyword vector and column vector\n",
    "            sim = cosine_similarity(keyword_vector, avg_col_vector)[0][0]\n",
    "            # If the similarity is above the threshold, increment the count\n",
    "            if sim > threshold:\n",
    "                similarity_count += 1\n",
    "                \n",
    "    return similarity_count\n",
    "\n",
    "# Creating a list to store the results\n",
    "result_df = []\n",
    "\n",
    "# Convert keywords into Word2Vec vectors\n",
    "keyword_vectors = {kw: average_word_vectors(tokenize(kw), model, vocab, 100).reshape(1, -1) for kw in keywords}\n",
    "\n",
    "for seed_paper_category in papers['seed_paper'].unique():\n",
    "    for kw, kw_vector in keyword_vectors.items():\n",
    "        frequency = keyword_similarity(seed_paper_category, kw_vector, threshold=0.8)\n",
    "        result_df.append({'seed_paper': seed_paper_category,\n",
    "                          'keyword': kw,\n",
    "                          'similarity_count': frequency})\n",
    "\n",
    "# Convert the list of dictionaries into a DataFrame\n",
    "result_df = pd.DataFrame(result_df)\n",
    "\n",
    "# Create a pivot table with 'keyword' as the index, 'seed_paper' as columns, and 'similarity_count' as values\n",
    "pivot_table = result_df.pivot_table(index='keyword', columns='seed_paper', values='similarity_count', aggfunc='sum')\n",
    "\n",
    "# Display the pivot table\n",
    "pivot_table\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# Function to calculate cosine similarity and filter papers\n",
    "def filter_papers_by_keyword_similarity(df, keywords, threshold=0.5, columns=['Title', 'Abstract', 'Author Keywords']):\n",
    "    # Tokenize and vectorize the keywords\n",
    "    keyword_vectors = {kw: average_word_vectors(tokenize(kw), model, set(model.wv.index_to_key), model.vector_size)\n",
    "                       for kw in keywords}\n",
    "\n",
    "    # Helper function to calculate the maximum similarity of a text to any keyword\n",
    "    def max_similarity_to_keywords(text, keyword_vectors):\n",
    "        tokens = tokenize(text)\n",
    "        vectors = [model.wv[token] for token in tokens if token in model.wv]\n",
    "        if not vectors:\n",
    "            return 0\n",
    "        text_vector = np.mean(vectors, axis=0).reshape(1, -1)\n",
    "        max_sim = max(cosine_similarity(text_vector, np.array(list(keyword_vectors.values())))[0])\n",
    "        return max_sim\n",
    "\n",
    "    # List to keep track of rows with similarity above threshold\n",
    "    rows_above_threshold = []\n",
    "\n",
    "    # Iterate over each row in the DataFrame\n",
    "    for _, row in df.iterrows():\n",
    "        row_similarity = 0\n",
    "        # Check similarity for specified columns\n",
    "        for col in columns:\n",
    "            col_similarity = max_similarity_to_keywords(str(row[col]), keyword_vectors)\n",
    "            row_similarity = max(row_similarity, col_similarity)\n",
    "        # If similarity for any column is above the threshold, add the row to the list\n",
    "        if row_similarity > threshold:\n",
    "            rows_above_threshold.append(row)\n",
    "\n",
    "    # Return a DataFrame with only the rows above the similarity threshold\n",
    "    return pd.DataFrame(rows_above_threshold)\n",
    "\n",
    "# Example usage:\n",
    "# papers is your original DataFrame, keywords is your list of keywords, and threshold is the similarity threshold\n",
    "filtered_papers = filter_papers_by_keyword_similarity(papers, keywords, threshold=0.8)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Authors</th>\n",
       "      <th>Author full names</th>\n",
       "      <th>Author(s) ID</th>\n",
       "      <th>Title</th>\n",
       "      <th>Year</th>\n",
       "      <th>Source title</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Issue</th>\n",
       "      <th>Art. No.</th>\n",
       "      <th>Page start</th>\n",
       "      <th>...</th>\n",
       "      <th>Index Keywords</th>\n",
       "      <th>Document Type</th>\n",
       "      <th>Publication Stage</th>\n",
       "      <th>Open Access</th>\n",
       "      <th>Source</th>\n",
       "      <th>EID</th>\n",
       "      <th>seed_paper</th>\n",
       "      <th>tokenized_abstracts</th>\n",
       "      <th>avg_word2vec</th>\n",
       "      <th>tfidf_weighted_word2vec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Enders A.; Klofstad C.; Stoler J.; Uscinski J.E.</td>\n",
       "      <td>Enders, Adam (57200699056); Klofstad, Casey (1...</td>\n",
       "      <td>57200699056; 10340526300; 26533535500; 2417218...</td>\n",
       "      <td>How Anti-Social Personality Traits and Anti-Es...</td>\n",
       "      <td>2023</td>\n",
       "      <td>American Politics Research</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>247</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>All Open Access; Green Open Access</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-85142176957</td>\n",
       "      <td>nature_of_belief</td>\n",
       "      <td>[conspiracy, theories, misinformation, ctm, be...</td>\n",
       "      <td>[0.0017258335, 0.6096328, -0.068927474, -0.076...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Groenendyk E.; Kimbrough E.O.; Pickup M.</td>\n",
       "      <td>Groenendyk, Eric (6506537141); Kimbrough, Erik...</td>\n",
       "      <td>6506537141; 35484426400; 15757997300</td>\n",
       "      <td>How Norms Shape the Nature of Belief Systems i...</td>\n",
       "      <td>2023</td>\n",
       "      <td>American Journal of Political Science</td>\n",
       "      <td>67</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>623</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-85134518364</td>\n",
       "      <td>nature_of_belief</td>\n",
       "      <td>[ideology, understood, concerned, americans, l...</td>\n",
       "      <td>[0.022352302, 0.6387013, 0.07324514, -0.117399...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Rettig L.; Gärtner L.; Schoen H.</td>\n",
       "      <td>Rettig, Leonie (58040381000); Gärtner, Lea (57...</td>\n",
       "      <td>58040381000; 57218796116; 7006886875</td>\n",
       "      <td>Facing trade-offs: The variability of public s...</td>\n",
       "      <td>2023</td>\n",
       "      <td>Environmental Science and Policy</td>\n",
       "      <td>147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>244</td>\n",
       "      <td>...</td>\n",
       "      <td>Germany; climate change; conflict management; ...</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>All Open Access; Bronze Open Access</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-85164024846</td>\n",
       "      <td>nature_of_belief</td>\n",
       "      <td>[policy, support, often, measured, asking, res...</td>\n",
       "      <td>[0.058773663, 0.57156825, -0.023501812, -0.133...</td>\n",
       "      <td>[-0.24882661, 0.6500706, 0.39193612, -0.008946...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Enninga J.; Yonk R.M.</td>\n",
       "      <td>Enninga, Justus (58220513900); Yonk, Ryan M. (...</td>\n",
       "      <td>58220513900; 35294612100</td>\n",
       "      <td>Achieving Ecological Reflexivity: The Limits o...</td>\n",
       "      <td>2023</td>\n",
       "      <td>Sustainability (Switzerland)</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>6396</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>democracy; ecological economics; environmental...</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>All Open Access; Gold Open Access</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-85156147516</td>\n",
       "      <td>nature_of_belief</td>\n",
       "      <td>[environmental, problems, often, highly, compl...</td>\n",
       "      <td>[-0.057574674, 0.36406302, 0.2366163, -0.10191...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Mongrain P.</td>\n",
       "      <td>Mongrain, Philippe (57202605696)</td>\n",
       "      <td>57202605696</td>\n",
       "      <td>With a little help from my friends? The impact...</td>\n",
       "      <td>2023</td>\n",
       "      <td>European Journal of Political Research</td>\n",
       "      <td>62</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1320</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-85144013284</td>\n",
       "      <td>nature_of_belief</td>\n",
       "      <td>[partisan, preferences, usually, stand, major,...</td>\n",
       "      <td>[0.04492471, 0.6141413, 0.10414245, -0.1255418...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6346</th>\n",
       "      <td>Chan S.</td>\n",
       "      <td>Chan, Steve (8271623500)</td>\n",
       "      <td>8271623500</td>\n",
       "      <td>Rationality, Bureaucratic Politics and Belief ...</td>\n",
       "      <td>1979</td>\n",
       "      <td>Journal of Peace Research</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>333</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-84970399843</td>\n",
       "      <td>structure_of_decision</td>\n",
       "      <td>[understanding, belief, systems, foreign, poli...</td>\n",
       "      <td>[-0.05347046, 0.36750832, 0.19520402, 0.038313...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6347</th>\n",
       "      <td>Eden C.; Sims D.</td>\n",
       "      <td>Eden, Colin (7006244321); Sims, David (7101600...</td>\n",
       "      <td>7006244321; 7101600761</td>\n",
       "      <td>On the nature of problems in consulting practice</td>\n",
       "      <td>1979</td>\n",
       "      <td>Omega</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>119</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-0001098817</td>\n",
       "      <td>structure_of_decision</td>\n",
       "      <td>[consultants, wish, help, clients, complex, po...</td>\n",
       "      <td>[-0.055449385, 0.3924494, 0.24187995, -0.07019...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6348</th>\n",
       "      <td>Eden C.</td>\n",
       "      <td>Eden, Colin (7006244321)</td>\n",
       "      <td>7006244321</td>\n",
       "      <td>Modelling the influence of decision makers on ...</td>\n",
       "      <td>1977</td>\n",
       "      <td>Futures</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>272</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-34247992941</td>\n",
       "      <td>structure_of_decision</td>\n",
       "      <td>[article, describes, attempts, use, futures, r...</td>\n",
       "      <td>[-0.14518416, 0.36221546, 0.3672079, -0.110461...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6352</th>\n",
       "      <td>Lendaris G.G.</td>\n",
       "      <td>Lendaris, George G. (7003527565)</td>\n",
       "      <td>7003527565</td>\n",
       "      <td>Structural Modeling—A Tutorial Guide</td>\n",
       "      <td>1980</td>\n",
       "      <td>IEEE Transactions on Systems, Man, and Cyberne...</td>\n",
       "      <td>SMC-10</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>807</td>\n",
       "      <td>...</td>\n",
       "      <td>STRUCTURAL MODELING; model; nonbiological mode...</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-0019231375</td>\n",
       "      <td>structure_of_decision</td>\n",
       "      <td>[structural, modeling, sm, refers, modeling, a...</td>\n",
       "      <td>[-0.07940591, 0.3570396, 0.47081923, -0.158389...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6355</th>\n",
       "      <td>Heradstveit D.; Narvesen O.</td>\n",
       "      <td>Heradstveit, Daniel (6603236211); Narvesen, Ov...</td>\n",
       "      <td>6603236211; 57190058904</td>\n",
       "      <td>Psychological Constraints on Decision-making. ...</td>\n",
       "      <td>1978</td>\n",
       "      <td>Cooperation and Conflict</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Article</td>\n",
       "      <td>Final</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Scopus</td>\n",
       "      <td>2-s2.0-70349393127</td>\n",
       "      <td>structure_of_decision</td>\n",
       "      <td>[operational, code, cognitive, map, approaches...</td>\n",
       "      <td>[-0.18945162, 0.50257474, 0.4241818, -0.081269...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3034 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Authors  \\\n",
       "1     Enders A.; Klofstad C.; Stoler J.; Uscinski J.E.   \n",
       "4             Groenendyk E.; Kimbrough E.O.; Pickup M.   \n",
       "7                     Rettig L.; Gärtner L.; Schoen H.   \n",
       "8                                Enninga J.; Yonk R.M.   \n",
       "11                                         Mongrain P.   \n",
       "...                                                ...   \n",
       "6346                                           Chan S.   \n",
       "6347                                  Eden C.; Sims D.   \n",
       "6348                                           Eden C.   \n",
       "6352                                     Lendaris G.G.   \n",
       "6355                       Heradstveit D.; Narvesen O.   \n",
       "\n",
       "                                      Author full names  \\\n",
       "1     Enders, Adam (57200699056); Klofstad, Casey (1...   \n",
       "4     Groenendyk, Eric (6506537141); Kimbrough, Erik...   \n",
       "7     Rettig, Leonie (58040381000); Gärtner, Lea (57...   \n",
       "8     Enninga, Justus (58220513900); Yonk, Ryan M. (...   \n",
       "11                     Mongrain, Philippe (57202605696)   \n",
       "...                                                 ...   \n",
       "6346                           Chan, Steve (8271623500)   \n",
       "6347  Eden, Colin (7006244321); Sims, David (7101600...   \n",
       "6348                           Eden, Colin (7006244321)   \n",
       "6352                   Lendaris, George G. (7003527565)   \n",
       "6355  Heradstveit, Daniel (6603236211); Narvesen, Ov...   \n",
       "\n",
       "                                           Author(s) ID  \\\n",
       "1     57200699056; 10340526300; 26533535500; 2417218...   \n",
       "4                  6506537141; 35484426400; 15757997300   \n",
       "7                  58040381000; 57218796116; 7006886875   \n",
       "8                              58220513900; 35294612100   \n",
       "11                                          57202605696   \n",
       "...                                                 ...   \n",
       "6346                                         8271623500   \n",
       "6347                             7006244321; 7101600761   \n",
       "6348                                         7006244321   \n",
       "6352                                         7003527565   \n",
       "6355                            6603236211; 57190058904   \n",
       "\n",
       "                                                  Title  Year  \\\n",
       "1     How Anti-Social Personality Traits and Anti-Es...  2023   \n",
       "4     How Norms Shape the Nature of Belief Systems i...  2023   \n",
       "7     Facing trade-offs: The variability of public s...  2023   \n",
       "8     Achieving Ecological Reflexivity: The Limits o...  2023   \n",
       "11    With a little help from my friends? The impact...  2023   \n",
       "...                                                 ...   ...   \n",
       "6346  Rationality, Bureaucratic Politics and Belief ...  1979   \n",
       "6347   On the nature of problems in consulting practice  1979   \n",
       "6348  Modelling the influence of decision makers on ...  1977   \n",
       "6352               Structural Modeling—A Tutorial Guide  1980   \n",
       "6355  Psychological Constraints on Decision-making. ...  1978   \n",
       "\n",
       "                                           Source title  Volume Issue  \\\n",
       "1                            American Politics Research      51     2   \n",
       "4                 American Journal of Political Science      67     3   \n",
       "7                      Environmental Science and Policy     147   NaN   \n",
       "8                          Sustainability (Switzerland)      15     8   \n",
       "11               European Journal of Political Research      62     4   \n",
       "...                                                 ...     ...   ...   \n",
       "6346                          Journal of Peace Research      16     4   \n",
       "6347                                              Omega       7     2   \n",
       "6348                                            Futures       9     4   \n",
       "6352  IEEE Transactions on Systems, Man, and Cyberne...  SMC-10    12   \n",
       "6355                           Cooperation and Conflict      13     2   \n",
       "\n",
       "     Art. No. Page start  ...  \\\n",
       "1         NaN        247  ...   \n",
       "4         NaN        623  ...   \n",
       "7         NaN        244  ...   \n",
       "8        6396        NaN  ...   \n",
       "11        NaN       1320  ...   \n",
       "...       ...        ...  ...   \n",
       "6346      NaN        333  ...   \n",
       "6347      NaN        119  ...   \n",
       "6348      NaN        272  ...   \n",
       "6352      NaN        807  ...   \n",
       "6355      NaN         77  ...   \n",
       "\n",
       "                                         Index Keywords  Document Type  \\\n",
       "1                                                   NaN        Article   \n",
       "4                                                   NaN        Article   \n",
       "7     Germany; climate change; conflict management; ...        Article   \n",
       "8     democracy; ecological economics; environmental...        Article   \n",
       "11                                                  NaN        Article   \n",
       "...                                                 ...            ...   \n",
       "6346                                                NaN        Article   \n",
       "6347                                                NaN        Article   \n",
       "6348                                                NaN        Article   \n",
       "6352  STRUCTURAL MODELING; model; nonbiological mode...        Article   \n",
       "6355                                                NaN        Article   \n",
       "\n",
       "      Publication Stage                          Open Access  Source  \\\n",
       "1                 Final   All Open Access; Green Open Access  Scopus   \n",
       "4                 Final                                  NaN  Scopus   \n",
       "7                 Final  All Open Access; Bronze Open Access  Scopus   \n",
       "8                 Final    All Open Access; Gold Open Access  Scopus   \n",
       "11                Final                                  NaN  Scopus   \n",
       "...                 ...                                  ...     ...   \n",
       "6346              Final                                  NaN  Scopus   \n",
       "6347              Final                                  NaN  Scopus   \n",
       "6348              Final                                  NaN  Scopus   \n",
       "6352              Final                                  NaN  Scopus   \n",
       "6355              Final                                  NaN  Scopus   \n",
       "\n",
       "                     EID             seed_paper  \\\n",
       "1     2-s2.0-85142176957       nature_of_belief   \n",
       "4     2-s2.0-85134518364       nature_of_belief   \n",
       "7     2-s2.0-85164024846       nature_of_belief   \n",
       "8     2-s2.0-85156147516       nature_of_belief   \n",
       "11    2-s2.0-85144013284       nature_of_belief   \n",
       "...                  ...                    ...   \n",
       "6346  2-s2.0-84970399843  structure_of_decision   \n",
       "6347   2-s2.0-0001098817  structure_of_decision   \n",
       "6348  2-s2.0-34247992941  structure_of_decision   \n",
       "6352   2-s2.0-0019231375  structure_of_decision   \n",
       "6355  2-s2.0-70349393127  structure_of_decision   \n",
       "\n",
       "                                    tokenized_abstracts  \\\n",
       "1     [conspiracy, theories, misinformation, ctm, be...   \n",
       "4     [ideology, understood, concerned, americans, l...   \n",
       "7     [policy, support, often, measured, asking, res...   \n",
       "8     [environmental, problems, often, highly, compl...   \n",
       "11    [partisan, preferences, usually, stand, major,...   \n",
       "...                                                 ...   \n",
       "6346  [understanding, belief, systems, foreign, poli...   \n",
       "6347  [consultants, wish, help, clients, complex, po...   \n",
       "6348  [article, describes, attempts, use, futures, r...   \n",
       "6352  [structural, modeling, sm, refers, modeling, a...   \n",
       "6355  [operational, code, cognitive, map, approaches...   \n",
       "\n",
       "                                           avg_word2vec  \\\n",
       "1     [0.0017258335, 0.6096328, -0.068927474, -0.076...   \n",
       "4     [0.022352302, 0.6387013, 0.07324514, -0.117399...   \n",
       "7     [0.058773663, 0.57156825, -0.023501812, -0.133...   \n",
       "8     [-0.057574674, 0.36406302, 0.2366163, -0.10191...   \n",
       "11    [0.04492471, 0.6141413, 0.10414245, -0.1255418...   \n",
       "...                                                 ...   \n",
       "6346  [-0.05347046, 0.36750832, 0.19520402, 0.038313...   \n",
       "6347  [-0.055449385, 0.3924494, 0.24187995, -0.07019...   \n",
       "6348  [-0.14518416, 0.36221546, 0.3672079, -0.110461...   \n",
       "6352  [-0.07940591, 0.3570396, 0.47081923, -0.158389...   \n",
       "6355  [-0.18945162, 0.50257474, 0.4241818, -0.081269...   \n",
       "\n",
       "                                tfidf_weighted_word2vec  \n",
       "1     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "4     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "7     [-0.24882661, 0.6500706, 0.39193612, -0.008946...  \n",
       "8     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "11    [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "...                                                 ...  \n",
       "6346  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "6347  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "6348  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "6352  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "6355  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "\n",
       "[3034 rows x 27 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_papers\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Topic Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 18/18 [01:52<00:00,  6.26s/it]\n"
     ]
    },
    {
     "data": {
      "image/png": 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W7f/jjz8CQINvoOI3pvq5UVFRURgxYoT+544dO6JHjx74+++/9ds+//xzjBgxAl5eXsjJydHfYmNjodFo8Ouvvxqc85577jGI9PPy8rB//35MnTpVHyXn5OQgNzcX48aNQ0JCAtLT0w3O8dBDDxl0s44YMQIajQYpKSkAdD09xcXFeOGFFxoMn4nHnT59GgkJCfjHP/6B3Nxc/eOWlpZi7Nix+PXXXxskHtfl7++PM2fO4JFHHkF+fj42b96Mf/zjH/Dz88PLL7+s7w06deoUkpKS8PTTTzfIrxHbkpGRgdOnT2P27Nnw9vbW39+nTx/ceuut+t9bXY888ojBz7t27YJWq8XUqVMNfg8BAQHo1q0bDhw4AAD6nqU9e/agrKysyetrqb1796KqqgpPP/20wbeZBQsWwN3dvcFrysnJCXPmzGnz4wLQJ+QXFxeb5HyAdb6e22rlypXIzMzE5s2bTXI+QNfzWbeXcsiQIQCABx980CAnZciQIaiqqmpwzUFBQQY9cO7u7pg5cyZOnTqFzMxMAG3/XQC6/LyrV6/ijz/+MOr6fvzxRwQEBOD+++/Xb3N0dMSTTz6JkpISHDp0yKjz3UhrPgMef/xxg5+feOIJfdub8uWXXyImJqZB7ydQ+3nU2uesrTw9PXHhwgUkJCQYfWxsbCwiIiL0P/fp0wfu7u76v1UajQZ79+7F5MmTERQUpN+va9euGD9+fNsb34iNGzfil19+MbiZS1OfhcuXL2/QhoCAgBaf126H7dzd3QG0/I9HSkoK5HJ5g5lSAQEB8PT0bPBh3alTpwbn8PLyQn5+vv7nhIQEnD17tsmuTzHJWFR/9kFiYiIEQcCyZcuwbNmyJs8RHBzcZLu8vLwAQN+uK1euAGh+ZoP4Bm1uyK2wsFB/7sYEBgZi06ZNeO+995CQkIA9e/bgjTfewPLlyxEYGIj58+e3qC3i896jR48G9/Xs2RN79uxpkBRe/3lMSEiAIAjo1q1bo48hDp117twZixYtwrp16/Dpp59ixIgRmDhxIh588MEmh+ya01TblUolunTp0uA1FRwcbLIkXrEcREu/PLSENb6e22rkyJEYM2YM1q5d2yDobq36bRZfO3VnPtbdXv9aunbt2iDPpHv37gB0OUwBAQFt/l0AwPPPP4+9e/di8ODB6Nq1K2677Tb84x//wPDhw5u9vpSUFHTr1q3B8EbPnj3195tSaz4D6r/XIyIiIJfLm825unLlCu65555m29La56ytVq9ejUmTJqF79+7o3bs3br/9dsyYMcMg0b4pN/pblZ2djfLy8kZnCZtr5vDgwYObTBjPy8tDVVWV/mdnZ+dWff6KmvosjI6ORmxsbKvPa9fBU1BQEM6fP2/UcS1NjlMoFI1uF3tVAECr1eLWW2/Fc8891+i+4geiyNnZ2eBnsXdn8eLFGDduXKPnqP/ibkm7bkR83DfffBN9+/ZtdJ+WlhqQyWTo3r07unfvjjvvvBPdunXDp59+ivnz57e4PcZq7HmUyWT46aefGn1+6l7Lv/71L8yePRvffPMNfv75Zzz55JP6vImQkBCztbmxdreF+Lo35Yefrb6eb2TFihUYPXo03n///UZnGIoJ9PVpNJpGz9dUm015LW39XQC6wCM+Ph7ff/89du/ejS+//BLvvfceli9fjlWrVhndJmtmqqRnSz1n9V9bI0eOxJUrV/SfSx988AH+/e9/Y/PmzTf8LLXEe8iUpkyZYtB7OWvWLKPquNV3/vx5+Pn56TtUTMVugycAuOuuu/Cf//wHR48exdChQ5vdNywsDFqtFgkJCfpvUICu0nJBQQHCwsKMfvyIiAiUlJS0Orrt0qULAF3PSFsi5PptAnQvqKb+sIr7uLu7m+xxAd31eHl5ISMjo0Fbmnoc8XmPj49vcN+lS5fg6+t7w1IEEREREAQBnTt3bvBHpTHR0dGIjo7GSy+9hN9//x3Dhw/H5s2b8corr9zw2KbaLv4uAaCqqgpJSUkmfW7rE2uvNBWktIY1vp6Btv9hHDVqFEaPHq3vGa3Py8vLYDheZOoeFpHYQ1f3ui5fvgygtg5NW38XIldXV0ybNg3Tpk1DVVUVpkyZgldffRVLly5tMKwvCgsLw9mzZ6HVag16ny5duqS/35Ra8xmQkJBg0NuWmJgIrVbbbB2fiIiIFn3Zbs1z1hQvL68GM2+rqqr0n5F1eXt7Y86cOZgzZw5KSkowcuRIrFy5ss1fRP38/KBSqZCYmNjgvsa2mdu//vUvg97YukOJxjp69CiuXLlilpIXdpvzBADPPfccXF1dMX/+fGRlZTW4/8qVK/op8XfccQcANJilsm7dOgDAnXfeafTjT506FUePHsWePXsa3FdQUIDq6upmj/fz89N/I27szdRYCYIbue222+Dm5oY1a9Y0mLorfhMZMGAAIiIi8NZbbzVaDfxGj3v8+HGUlpY22H7ixAnk5ubqu9/79++Pzp07Y/369Q0+QMS2BAYGom/fvvjwww8N9jl//jx+/vln/e+tOVOmTIFCocCqVasafNsSBAG5ubkAdHly9X8n0dHRkMvlLa7YXVdsbCyUSiXeeecdg8fdunUrCgsLW/Waaon//ve/+OCDDzB06FCMHTvWZOe1xtczoPtjVv/1Yywx96mxgrkRERG4dOmSQfvOnDnTorIdrXHt2jWD2b1FRUX46KOP0LdvX31ORlt/FwD0r3uRUqlEVFQUBEGAWq1u8rg77rgDmZmZBjN4q6ursWHDBnTo0AGjRo264WMbozWfARs3bjT4ecOGDQDQbA7PPffcgzNnzjSYWQ3Ufh619jlrSkRERIP8tP/85z8Nep7qP26HDh3QtWvXVn0u1adQKBAbG4uvv/4a165d029PTEzETz/91ObzG2vAgAGIjY3V325UJ6wpKSkpmD17NpRKJZYsWWLiVtp5z1NERAT++9//Ytq0aejZs6dBhfHff/9dP70WAGJiYjBr1iz85z//QUFBAUaNGoUTJ07gww8/xOTJkzFmzBijH3/JkiX49ttvcdddd+nLGJSWluLcuXP44osvkJycfMPpmRs3bsTNN9+M6OhoLFiwAF26dEFWVhaOHj2Kq1ev4syZM0a1yd3dHf/+978xf/58DBo0SF8P6cyZMygrK8OHH34IuVyODz74AOPHj0evXr0wZ84cBAcHIz09HQcOHIC7uzu+++67Jh/j448/xqeffoq7774bAwYMgFKpxMWLF7Ft2zaoVCp9/SK5XI5NmzZhwoQJ6Nu3L+bMmYPAwEBcunQJFy5c0P9hePPNNzF+/HgMHToU8+bN009T9vDwaLRGSn0RERF45ZVXsHTpUiQnJ2Py5Mlwc3NDUlISvvrqKzz00ENYvHgx9u/fj4ULF+K+++5D9+7dUV1djY8//hgKheKGuRCN6dixI5YuXYpVq1bh9ttvx8SJExEfH4/33nsPgwYNMsm3oS+++AIdOnTQJx7v2bMHR44cQUxMDD7//PM2n78ua3w9A7oP202bNuGVV15B165d4efnh1tuucWoc4waNQqjRo1qNNl57ty5WLduHcaNG4d58+YhOzsbmzdvRq9evfQTU0ype/fumDdvHv744w/4+/tj27ZtyMrKwvbt2/X7mOJ3cdtttyEgIADDhw+Hv78/Ll68iHfffRd33nlns7lyDz30EN5//33Mnj0bJ0+eRHh4OL744gscOXIE69evN2mencjYz4CkpCRMnDgRt99+O44ePYpPPvkE//jHPxoUSqxryZIl+OKLL3Dfffdh7ty5GDBgAPLy8vDtt99i8+bNiImJafFzJpPJMGrUKBw8eLDZ65o/fz4eeeQR3HPPPbj11ltx5swZ7Nmzp8HvLioqCqNHj8aAAQPg7e2NP//8E1988YXBUiVtsXLlSvz8888YPnw4Hn30UWg0Grz77rvo3bs3Tp8+bZLHMKe4uDh88skn0Gq1KCgowB9//IEvv/wSMpkMH3/8cYtyw4zW4nl5Nuzy5cvCggULhPDwcEGpVApubm7C8OHDhQ0bNhhME1ar1cKqVauEzp07C46OjkJoaKiwdOlSg30Eoenpt41NaS4uLhaWLl0qdO3aVVAqlYKvr68wbNgw4a233hKqqqoEQbjxdMsrV64IM2fOFAICAgRHR0chODhYuOuuu4QvvvhCv09jU6wFoXZa5oEDBwy2f/vtt8KwYcMEZ2dnwd3dXRg8eLDwv//9z2CfU6dOCVOmTBF8fHwEJycnISwsTJg6daqwb9++xp/oGmfPnhWWLFki9O/fX/D29hYcHByEwMBA4b777hPi4uIa7H/48GHh1ltvFdzc3ARXV1ehT58+BtNoBUEQ9u7dKwwfPlzf3gkTJgh//fWXwT43mr7/5ZdfCjfffLPg6uoquLq6CpGRkcLjjz8uxMfHC4IgCH///bcwd+5cISIiQlCpVIK3t7cwZswYYe/evc1e740e+9133xUiIyMFR0dHwd/fX3j00UcblGYYNWqUUVOgxccTbyqVSggJCRHuuusuYdu2bQ1es4LQ9lIFgmCdr+fMzEzhzjvvFNzc3AQANyxb0NS11Z3CXP9xP/nkE6FLly6CUqkU+vbtK+zZs6fJUgX1r1s8b/0SGI1do/j72LNnj9CnTx/ByclJiIyMbLR8Rlt/F++//74wcuRI/fs7IiJCWLJkiX5Kd3OysrKEOXPmCL6+voJSqRSio6OF7du3N9jPVKUKBMG4z4C//vpLuPfeewU3NzfBy8tLWLhwYYPSLPXLAQiCIOTm5goLFy4UgoODBaVSKYSEhAizZs0ScnJyBEFo2XNWXFwsABCmT59+w2vVaDTC888/L/j6+gouLi7CuHHjhMTExAZte+WVV4TBgwcLnp6egrOzsxAZGSm8+uqr+t9z3Wuvq6nXemPXvm/fPqFfv36CUqkUIiIihA8++EB49tlnBZVKdcPrqKslpQrqv79aqv71iK9v8ebg4CB4e3sLQ4YMEZYuXSqkpKQ0OEdT70djyWoaREREZNPEorTXr183S9HFlvjxxx9x11134cyZM4iOjpakDaYyefLkVpdIsHd2nfNERERkSQcOHMD06dNtLnCqv0xRQkICfvzxxxYtd9Qe2XXOExERkSW9+eabUjehVbp06YLZs2fra9Bt2rQJSqWyyXIY7R2DJyIionbu9ttvx//+9z9kZmbCyckJQ4cOxWuvvdZkceH2jjlPREREREZgzhMRERGRERg8ERERERmBOU+N0Gq1uHbtGtzc3Ey2JhIRERGZlyAIKC4uRlBQUIPFq02JwVMjrl271mAFdCIiIrINaWlpZl3MncFTI8Qy+2lpaSZfiZmIiIjMo6ioCKGhoWZZJqguBk+NEIfq3N3dGTwRERHZGHOn3DBhnIiIiMgIDJ6IiIiIjMDgiYiIiMgIzHkiIiKyMxqNBmq1WupmmJyjoyMUCoXUzWDwREREZC8EQUBmZiYKCgqkborZeHp6IiAgQNI6jAyeiIiI7IQYOPn5+cHFxcWuCj0LgoCysjJkZ2cDAAIDAyVrC4MnIiIiO6DRaPSBk4+Pj9TNMQtnZ2cAQHZ2Nvz8/CQbwmPCOBERkR0Qc5xcXFwkbol5idcnZU4XgyciIiI7Yk9DdY2xhutj8ERERERkBAZPREREREZg8ERERERkBM62s6AKtQY5JZVQKuTwc1dJ3RwiIiJqBfY8WdB7BxJx8xsH8M7+BKmbQmQ2a368iOGv70d6QbnUTSEiG6LVarF27Vp07doVTk5O6NSpE1599VWpm9Uo9jxZkJerEgCQX2p/JfOJAKCyWoOPjqagXK3B16fS8fiYrlI3iahdEwQB5WqNJI/t7Kgwambc0qVLsWXLFvz73//GzTffjIyMDFy6dMmMLWw9Bk8W5F0TPOWVVkncEiLzOJmcr/+g3n8pm8ETkcTK1RpELd8jyWP/tXocXJQtCzOKi4vx9ttv491338WsWbMAABEREbj55pvN2cRW47CdBYnBU34ZgyeyT4cSruv/fyo1n18UiKhFLl68iMrKSowdO1bqprQIe54syMtFFzzl8g8K2alfL+cAAOQyQCsAhy5n4+5+IRK3iqj9cnZU4K/V4yR77BbvW7Psiq1gz5MF6XueSqsgCILErSEyreyiClzMKIJMBkwdGAoA2H/p+g2OIiJzkslkcFE6SHIzJt+pW7ducHZ2xr59+8z4bJgOgycLEoOnaq2A4spqiVtDZFq/Juh6naKDPXDvAF1v06H4bFRrtFI2i4hsgEqlwvPPP4/nnnsOH330Ea5cuYJjx45h69atUjetURy2syCVowIuSgXKqjTIL62Cu8pR6iYRmcyvl3W9TCO7dUS/Tl7wdHFEQZkaJ1PyMaSLfa7wTkSms2zZMjg4OGD58uW4du0aAgMD8cgjj0jdrEax58nCxLwnJtKSPdFqBRxO1PU8jezeEQq5DKO7dwSgm3VHRHQjcrkcL774IpKTk1FVVYWUlBQsXbpU6mY1isGThbFcAdmj89cKkVdahQ5ODujXyRMAcEtPfwAMnojI/jB4sjAvBk9khw7F64bshkX4wFGh+1gZ1U3XA5WQXYK0vDIpm0dEZFIMnizMh7WeyA79WlPfaVSPjvptHi6OGBDmBYC9T0RkX6wieNq4cSPCw8OhUqkwZMgQnDhxosl9d+zYAZlMZnBTqWoX2VWr1Xj++ecRHR0NV1dXBAUFYebMmbh27ZolLuWGanOeuEQL2YeiCjXiUgsA6JLF67ol0g8AsI/BExHZEcmDp507d2LRokVYsWIF4uLiEBMTg3HjxiE7u+kPW3d3d2RkZOhvKSkp+vvKysoQFxeHZcuWIS4uDrt27UJ8fDwmTpxoicu5IW9X3Qy7fA7bkZ34PTEXGq2ALr6uCPV2MbhvbE3wdOxKLkpZnoPIIuy9jqA1XJ/kpQrWrVuHBQsWYM6cOQCAzZs344cffsC2bdvwwgsvNHqMTCZDQEBAo/d5eHjgl19+Mdj27rvvYvDgwUhNTUWnTp1MewFGEnOeWGWc7IU4ZDeye8cG93X164AQL2dczS/HkcQc3Nar8fctEbWdo6Puy3lZWZnNVew2RlmZLodSvF4pSBo8VVVV4eTJkwZTEeVyOWJjY3H06NEmjyspKUFYWBi0Wi369++P1157Db169Wpy/8LCQshkMnh6ejZ6f2VlJSorK/U/FxUVGX8xLeTtwpwnsh+CIOiTxUd2921wv0wmw9hIP3x4NAUH4rMZPBGZkUKhgKenp37kxsXFxagq39ZOEASUlZUhOzsbnp6eUChavvyLqUkaPOXk5ECj0cDf399gu7+/Py5dutToMT169MC2bdvQp08fFBYW4q233sKwYcNw4cIFhIQ0XEOroqICzz//PO6//364u7s3es41a9Zg1apVbb+gFqi7RAuRrfs7pxTpBeVQKuS4qYlCmGNqgqf9l7IhCIJdfZgTWRtxVKa51Bdb5+np2eTok6VIPmxnrKFDh2Lo0KH6n4cNG4aePXvi/fffx8svv2ywr1qtxtSpUyEIAjZt2tTkOZcuXYpFixbpfy4qKkJoaKjpG486dZ7Y80R2QKwqPqizF1yUjX+c3NTFB86OCmQVVeLCtSL0DvawZBOJ2hWZTIbAwED4+flBrba/iUmOjo6S9jiJJA2efH19oVAokJWVZbA9KyurxVGlo6Mj+vXrh8TERIPtYuCUkpKC/fv3N9nrBABOTk5wcnIy/gJaQcx5KixXo1qjhYNC8px9olaruyRLU1SOCgzv6ou9F7Ow/1I2gyciC1AoFFYRZNgrSf9yK5VKDBgwwGAVZa1Wi3379hn0LjVHo9Hg3LlzCAwM1G8TA6eEhATs3bsXPj7Ws66Wp7MuwU0QgIJy+/tWQO1HhVqDY3/nAWg8WbyusT11s+5Y74mI7IHkw3aLFi3CrFmzMHDgQAwePBjr169HaWmpfvbdzJkzERwcjDVr1gAAVq9ejZtuugldu3ZFQUEB3nzzTaSkpGD+/PkAdIHTvffei7i4OHz//ffQaDTIzMwEAHh7e0OpVEpzoTUcFHJ4ODuisFyN/NIq+HawTI8Xkan9mZyPcrUGfm5OiAxwa3bfMT10wdOZqwXIKank656IbJrkwdO0adNw/fp1LF++HJmZmejbty92796tTyJPTU2FXF7bQZafn48FCxYgMzMTXl5eGDBgAH7//XdERUUBANLT0/Htt98CAPr27WvwWAcOHMDo0aMtcl3N8XFVorBczSVayKbVLVFwoyTwAA8VegW548K1IhyMv457BzSc3EFEZCskD54AYOHChVi4cGGj9x08eNDg53//+9/497//3eS5wsPDraKAVnO8XJVATinLFZBN0+c73WDITnRLpB8uXCvC/ktZDJ6IyKYxW1kC4hItLJRJtiqrqAKXMoshkwEjujas79QYcamW3y7noKpaa87mERGZFYMnCXCJFrJ1Yq9Tn2AP/QzSG4kJ8YSPqxLFldX4MznPnM0jIjIrBk8SEP/YcHFgslWHjByyAwC5XIbRPTjrjohsH4MnCfi4cokWsl0arYDDiTkAjAuegNqhOwZPRGTLGDxJQMx54mw7skXn0gtRUKaGm8oB/UI9jTp2RHdfOMhl+DunFEk5peZpIBGRmTF4koB+iRYGT2SDxHyn4RG+RlfId1c5YlC4NwD2PhGR7WLwJAEvBk9kw4wtUVCfWG38AIMnIrJRDJ4k4O3CnCeyTYXlapxKKwAAjOzeshIF9Y2pyXs6npSLkspqUzWNiMhiGDxJwLuDLngqq9KgQq2RuDVELfd7Yg40WgFdOroixMulVefo4uuKcB8XqDUCDtdUKScisiUMniTg5uQAB7luOQv2PpEtEZdkGdXKITsAkMlk+t6nfRc5dEdEtofBkwRkMpk+7ym3hMET2QZBEPDr5daVKKhvbKRu7coD8deh1Vr3ckpERPUxeJII857I1ly5Xor0gnIoHeS4qbNPm841uLM3XJUK5JRU4lx6oYlaSERkGQyeJMJyBWRrxKrig8O94axUtOlcSgc5RnTT9V6xZAER2RoGTxIRgyeub0e2orZEQetm2dXHauNEZKsYPEnEq2Zx4Lwyrm9H1q9CrcHxpFwAwKjufiY55+hIXc/TufRCZBdVmOScRESWwOBJIt76JVoqJW4J0Y39kZyHCrUWAe4qdPfvYJJz+rmp0CfEAwBwIJ69T0RkOxg8ScRLP2zHnieyfuKQ3YhuvpDJZCY7L4fuiMgWMXiSCBPGyZYcauOSLE0Rg6ffEnJQWc2CsURkGxg8SUSfMM5SBWTlMgrLcTmrBDIZcHNX0ySLi3oHeaCjmxPKqjQ4kZRn0nMTEZkLgyeJeLmw54lsw281hTFjQjz1w82mIpfLMKaHrjeL1caJyFYweJJI3Z4nQWCFZbJehxLMM2QnuqWm2vj+S9l8LxCRTWDwJBExeFJrBBRzZXmyUhqtgMMJup6nUSaq71Tfzd184aiQITWvDFeul5rlMYiITInBk0RUjgq41FRpZqFMslZnrhagsFwNN5UDYkI8zfIYHZwccFMX3XIvBzjrjohsAIMnCTHviaydWKLg5q6+cFCY7+NiTA/drLt9l7LM9hhERKbC4ElCLFdA1k4MnkaZKd9JNLanLnj6MzkfheWsfUZE1o3Bk4S8GDyRFSssU+N0WgEA8yWLi8J8XNGloyuqtQJ+q0lQJyKyVgyeJOTtolvfjrWeyBoduZIDrQB09euAIE9nsz/eWFYbJyIbweBJQt6uTgCAPC7RQlboUHxNiYJu5u11Eo2pCZ4Oxl+HRsuSBURkvRg8ScjbtabnicN2ZGUEQcCv+vpO5ilRUN+gcG+4OTkgr7QKZ64WWOQxiYhag8GThMScp1wGT2RlErNLkFFYAaWDHEM6+1jkMR0Vcn1u1X5WGyciK8bgSULeLlzfjqyTuBDwkM7ecK6pR2YJtzDviYhsAIMnCYk9Txy2I2tzyEIlCuob3aMjZDLgr4wiZBSWW/SxiYhaisGThHzEUgXseSIrUqHW4ERSHgDzlyioz6eDE/qGegIADlxiyQIisk4MniQk9jwVlqtRrdFK3BoineNJeais1iLAXYVufh0s/vi39BCH7lhtnIisE4MnCXk662bbCQJQwKrKZCXEquIju/tCJpNZ/PFvqak2fiQxFxVqjcUfn4joRhg8SchBIYeHM8sVkHWpXZLFT5LHjwp0R4C7CuVqDY7+nStJG4iImsPgSWI+XKKFrMi1gnIkZJdALtMtBiwFmUymL5h5gLPuiMgKMXiSmH7GHZPGyQqIvU4xoZ7wqFk+SAriUi37LmZDEFhtnIisC4MniXm5iD1PzHki6emriltoSZamDOvqA6WDHOk1PWFERNaEwZPExCVa8korJW4JtXfVGi0OJ+QAsHyJgvpclA4YFqGrbL6P1caJyMoweJKYlyt7nsg6nLlaiKKKang4OyImxEPq5uirjTPviYisDYMnifkw54mshFhV/OauvnBQSP/RMKam3tOfKXko4PuDiKyI9J+Q7VxtzhP/OJC06tZ3sgah3i7o7t8BWqE2sCMisgYMniTmzVIFZAUKyqpw9moBAOnzneq6JdIfABcKJiLrYhXB08aNGxEeHg6VSoUhQ4bgxIkTTe67Y8cOyGQyg5tKpTLYRxAELF++HIGBgXB2dkZsbCwSEhLMfRmt4sXgiazA4cQcaAWgm18HBHo4S90cPTHv6dDl61zCiIishuTB086dO7Fo0SKsWLECcXFxiImJwbhx45Cd3fQ3TXd3d2RkZOhvKSkpBvevXbsW77zzDjZv3ozjx4/D1dUV48aNQ0VFhbkvx2jeLsx5IunVVhW3nl4nAOjfyRMezo4oKFPjVFqB1M0hIgJgBcHTunXrsGDBAsyZMwdRUVHYvHkzXFxcsG3btiaPkclkCAgI0N/8/f319wmCgPXr1+Oll17CpEmT0KdPH3z00Ue4du0avv76awtckXG8O+iCp7IqDdfxIkkIgqDPKbKmITtAt4SRGNBx6I6IrIWkwVNVVRVOnjyJ2NhY/Ta5XI7Y2FgcPXq0yeNKSkoQFhaG0NBQTJo0CRcuXNDfl5SUhMzMTINzenh4YMiQIc2eUypuTg5wkOsWX2XvE0nhclYJsooq4eQgx+DO3lI3p4GxNQsF72e9JyKyEpIGTzk5OdBoNAY9RwDg7++PzMzMRo/p0aMHtm3bhm+++QaffPIJtFothg0bhqtXrwKA/jhjzllZWYmioiKDm6XIZDJ93lNuCYMnsjxxyG5IFx+oHBUSt6ahUd07Qi4D4rOKcTW/TOrmEBFJP2xnrKFDh2LmzJno27cvRo0ahV27dqFjx454//33W33ONWvWwMPDQ38LDQ01YYtvjHlPJKXaJVmso0RBfZ4uSgwI8wLAgplEZB0kDZ58fX2hUCiQlZVlsD0rKwsBAQEtOoejoyP69euHxMREANAfZ8w5ly5disLCQv0tLS3N2EtpEy/9Ei0Mnsiyyqs0OJ6UBwAY3cO68p3qGlMz6455T0RkDSQNnpRKJQYMGIB9+/bpt2m1Wuzbtw9Dhw5t0Tk0Gg3OnTuHwMBAAEDnzp0REBBgcM6ioiIcP368yXM6OTnB3d3d4GZJPq5OAIB8Bk9kYceSclFVrUWQhwoRHTtI3Zwmja2p9/T7lVyUV3FiBRFJS/Jhu0WLFmHLli348MMPcfHiRTz66KMoLS3FnDlzAAAzZ87E0qVL9fuvXr0aP//8M/7++2/ExcXhwQcfREpKCubPnw9Al0P09NNP45VXXsG3336Lc+fOYebMmQgKCsLkyZOluMQb0vc8lXF9O7KsX+vMspPJZBK3pmnd/Tsg2NMZldVa/H4lR+rmEFE75yB1A6ZNm4br169j+fLlyMzMRN++fbF79259wndqairk8toYLz8/HwsWLEBmZia8vLwwYMAA/P7774iKitLv89xzz6G0tBQPPfQQCgoKcPPNN2P37t0NimlaC2/9Ei2VEreE2ptfrbREQX0ymQy3RPrh42Mp2HcpG2N7+t/4ICIiM5EJgiBI3QhrU1RUBA8PDxQWFlpkCG/7kSSs+u4v3BkdiI0P9Df74xEBQHpBOYa/vh9yGXBq2W3wcHGUuknNOnApG3N2/IFADxV+f+EWq+4pIyJpWOrvt+TDdsT17UgaYq9Tv05eVh84AcDQCB+oHOXIKKzAxYxiqZtDRO0YgycrIAZPLFVAlnQoXixRYN1DdiKVowLDI3TlFA7Ec9YdEUmHwZMV8HJhzxNZVrVGiyM1idcju1tnfafG3FJTbXzfxawb7ElEZD4MnqxA3Z4npqCRJZxOK0BxRTU8XRzRJ8RT6ua02JgeuuDpVFoBv2wQkWQYPFkBMXhSawQUV1ZL3BpqD8R8p+FdfaGQ207idZCnM3oGukMQgIMcuiMiiTB4sgIqRwVclLo1xVgokyzhUIJuyG6UjeQ71XVLpK7NrDZORFJh8GQlmPfUPpVWViMtz7KL3eaVVuHs1QIA1l/fqTG31FQbP3T5OtQarcStIaL2iMGTleCMu/bpkU9OYsTaA7hrw2/49HgKiivMX2X+cGIOBAHo4e+GAA/rLBzbnL6hnvB2VaK4ohonU/Klbg4RtUMMnqyEV03wlFvC4Km9KCxT43CibvjsfHoRXvzqPIa8tg8vfHkWZ9IKzDZ5oLaquO3MsqtLIZdhdHcO3RGRdBg8WQnvmiKF7HlqP/5IzoMgAGE+Llh2VxQiOrqirEqDz/5Iw6SNR3DnO4fx8bEUFJmwN0oQBPyWYBtLsjRnTKRu1h2DJyKSAoMnK+Ht6gQAyCvl4sDtxfGkXADAsAhfzLu5M/YuGoX/e3go7u4XDKWDHH9lFGHZ1+cx5NV9eP6Lszhtgt6o+KxiZBVVQuUox6Bwb1NchiRGdu8IhVyGxOwSpOZaNmeMiIjBk5Xwdq3peWLCeLtx7O88AMBNXXRBjEwmw+DO3vj3tL448c+xWH5XFLr5dUC5WoOdf6Zh8sYjuOOdw/j4aHKre6PEquI3dfGBylFhmguRgIezIwaGeQEA9l9iwUwisiwGT1ZCn/PE4KldKKpQ48K1QgDAkM4+De73dFFi7s2d8fMzI/HFI0MxpX8wnBzkuJhRhGXfXMDgV/diyednEJeab1Rv1K8JtrUkS3PG1lQb318TEBIRWYqD1A0gHW8XzrZrT04m50Nbk+/U3Iw3mUyGgeHeGBjujRV39cJXp67ivydScTmrBJ+fvIrPT15FZIAb7h/cCZP7BcPDuekFfsuqqvFHkm52mi3nO4kG1gw7JmZxkWAisiz2PFkJseeJw3btw7GafKchnVued+Th4ojZwztjz9Mj8eWjw3DvgBCoHOW4lFmMFd9ewJDX9mLx52dwMqXx3qjjf+ehSqNFsKczIjq6muxapBLi5QwAyCyqQFU16z0RkeWw58lK+NQET3nseWoXjtfkOzU2ZHcjMpkMA8K8MCDMC8vuisLXp9Lx3+OpiM8qxhcnr+KLk1fRw98N9w8Oxd39QuBRM5PzUJ0SBTKZ7SzJ0pSOHZzg5CBHZbUWmYUV6OTjInWTiKidYM+TlRB7ngrL1ahm1WS7VlpZjXPpNflOXdo2483D2RGzhoVj99MjsOuxYbivpjcqPqsYK7/7C4Nf24tF/3cafybn6es7jbKDITtAF0QG1/Q+Xc3njDsishz2PFkJz5pcFUEACsrV8O3gJHGLyFxOpuRDoxUQ4uWMEC/T9JbIZDL07+SF/p288NJdUfj2dDo+PZ6KS5nF2BWXjl1x6QB0BSaHdbXN4piNCfFywd/XS3E1v1zqphBRO8KeJyvhoJDrk32Z92Tfjv0t5jsZP2TXEh7OjpgxNBw/PTUCXz02DFMHhsC5pizBTV284a5qOqnc1oSw54mIJMCeJyvi7apEYbmaiwPbueNJNflObRyyuxGZTIZ+nbzQr6Y36uiVXPQL9TTrY1pabfDEnicishz2PFkRLg5s/8qrNDh7tQAAcJOZep4a465yxLheAfBzt72FgJsjDnsyeCIiS2LwZEW8amo9cYkW+xWXmg+1RkCghwqh3s5SN8fmcdiOiKTA4MmKiEu05JVWStwSMpfjf9fWd7KHcgFSY60nIpICgycrIpYrYM+T/TqWJK5nZ7khO3sm1nrSCkBmYYXUzSGidoLBkxXhEi32rUKtwem0AgDAEAZPJsFaT0QkBQZPVsRb3/PE4MkenUotQFW1Fn5uTghnNWyTYdI4EVkagycrwtl29u24uJ5dFx/mO5kQk8aJyNIYPFkRMecpt4TBkz2qXc/OvPWd2ht98FTAnicisgwGT1aEOU/2q7Jag7jUfAC6Kt9kOsGeLJRJRJbF4MmKeHfQBU9lVRpUqDUSt4ZM6ezVQlRWa+HbQYmIjh2kbo5dEXOe0hk8EZGFMHiyIm5ODnCQ63Jh2PtkX47XWc+O+U6mFVozbJdRWA61hrWeiMj8GDxZEZlMVqfWE4Mne2Kp9ezaI98OTlCy1hMRWRCDJyvj7cLgyd6oNVr8mazLdxpiwfXs2gu5XIaQmrynNM64IyILYPBkZbz0S7QweLIXZ68WolytgZeLI7r5Md/JHGoLZTLviYjMr9XBU2JiIvbs2YPyct2HlSAIJmtUe+bj6gQAyGfwZDfE+k6DO3tDLme+kzmwUCYRWZLRwVNubi5iY2PRvXt33HHHHcjIyAAAzJs3D88++6zJG9je6Hueyri+nb2ore/EITtzYaFMIrIko4OnZ555Bg4ODkhNTYWLS+0SE9OmTcPu3btN2rj2qDbnqVLilpApVGu0+DOZyeLmFsJhOyKyIAdjD/j555+xZ88ehISEGGzv1q0bUlJSTNaw9kqcbZdfyp4ne3DhWhFKqzRwVzkgMsBd6ubYLdZ6IiJLMrrnqbS01KDHSZSXlwcnJyeTNKo94+LA9qU238kHCuY7mQ1rPRGRJRkdPI0YMQIfffSR/meZTAatVou1a9dizJgxJm1ce8TFge3LsZp8Jy7JYl6s9URElmT0sN3atWsxduxY/Pnnn6iqqsJzzz2HCxcuIC8vD0eOHDFHG9sVL9Z5shsarYA/kpgsbglirae/c0qRll+GUO+GveNERKZidM9T7969cfnyZdx8882YNGkSSktLMWXKFJw6dQoRERHmaGO7UrfnieUfbNvFjCIUV1bDzckBUUHMdzI3sdYT856IyNyM6nlSq9W4/fbbsXnzZrz44ovmalO7JvY8qTUCiiur4a5ylLhF1FrHatazGxjuxXwnC2CtJyKyFKN6nhwdHXH27FlztYUAOCsVcHZUAGChTFtXu54dh+wsgeUKiMhSjB62e/DBB7F161ZztIVqcMad7dNqBfwh1nfqzGRxS2ChTCKyFKODp+rqamzatAkDBw7Eww8/jEWLFhncjLVx40aEh4dDpVJhyJAhOHHiRIuO++yzzyCTyTB58mSD7SUlJVi4cCFCQkLg7OyMqKgobN682eh2SYkz7mxffFYxCsrUcFUq0DvYQ+rmtAvseSIiSzF6tt358+fRv39/AMDly5cN7pPJjMvr2LlzJxYtWoTNmzdjyJAhWL9+PcaNG4f4+Hj4+fk1eVxycjIWL16MESNGNLhv0aJF2L9/Pz755BOEh4fj559/xmOPPYagoCBMnDjRqPZJRSyUmVvC4MlWiflOA8K94ajg+tuWIOY8ZRZVoFqjhQOfdyIyE6ODpwMHDpjswdetW4cFCxZgzpw5AIDNmzfjhx9+wLZt2/DCCy80eoxGo8EDDzyAVatW4bfffkNBQYHB/b///jtmzZqF0aNHAwAeeughvP/++zhx4oTNBE/eLrokcfY82a7a9ew4ZGcpHTs4QamQo0qjRUZhBcsVEJHZtOmr2dWrV3H16tVWHVtVVYWTJ08iNja2tjFyOWJjY3H06NEmj1u9ejX8/Pwwb968Ru8fNmwYvv32W6Snp0MQBBw4cACXL1/Gbbfd1uQ5KysrUVRUZHCTkpc+54lLtNgiQRBwIpnFMS1NLpfpyxVw6I6IzMno4Emr1WL16tXw8PBAWFgYwsLC4OnpiZdffhlabcuXRcjJyYFGo4G/v7/Bdn9/f2RmZjZ6zOHDh7F161Zs2bKlyfNu2LABUVFRCAkJgVKpxO23346NGzdi5MiRTR6zZs0aeHh46G+hoaEtvg5z8NGvb8eeJ1uUkF2CvNIqqBzliA72lLo57QqTxlvu46PJuO3fh5Cay+eKyFhGB08vvvgi3n33Xbz++us4deoUTp06hddeew0bNmzAsmXLzNFGAEBxcTFmzJiBLVu2wNfXt8n9NmzYgGPHjuHbb7/FyZMn8a9//QuPP/449u7d2+QxS5cuRWFhof6WlpZmjktoMX3PE4ftbNJxMd8pzAtKB+bdWBKTxlsmPrMYq777C5ezSvDVqXSpm0Nkc4zOefrwww/xwQcfGOQP9enTB8HBwXjsscfw6quvtug8vr6+UCgUyMrKMtielZWFgICABvtfuXIFycnJmDBhgn6b2NPl4OCA+Ph4BAUF4Z///Ce++uor3Hnnnfq2nT59Gm+99ZbBEGFdTk5OVrWosTeXaLFpx7gki2RYKPPGtFoBS3edRbVWt4JBXGq+xC0isj1Gfy3Oy8tDZGRkg+2RkZHIy8tr8XmUSiUGDBiAffv26bdptVrs27cPQ4cObfT8586dw+nTp/W3iRMnYsyYMTh9+jRCQ0OhVquhVqshlxtelkKhMGpIUWpeHLazWYIg6JPFb2JxTIvjsN2NfXoiFXGpBfqq96dS86HVcikoImMY3fMUExODd999F++8847B9nfffRcxMTFGnWvRokWYNWsWBg4ciMGDB2P9+vUoLS3Vz76bOXMmgoODsWbNGqhUKvTu3dvgeE9PTwDQb1cqlRg1ahSWLFkCZ2dnhIWF4dChQ/joo4+wbt06Yy9VMj4ctrNZV66XIqekEk4OcsSEsr6TpXHYrnmZhRVY+9MlAMDS8ZF46+d4FFVU4++cEnT1c5O4dUS2w+jgae3atbjzzjuxd+9efQ/R0aNHkZaWhh9//NGoc02bNg3Xr1/H8uXLkZmZib59+2L37t36JPLU1NQGvUg38tlnn2Hp0qV44IEHkJeXh7CwMLz66qt45JFHjDqPlMSep8JyNevV2JjjSbp8p36dPOHkoJC4Ne0Paz01b+W3F1BcWY2+oZ6YM7wzfr6QhRPJeYhLLWDwRGQEo4OnUaNGIT4+Hu+99x4uXdJ9g5kyZYq+EKWxFi5ciIULFzZ638GDB5s9dseOHQ22BQQEYPv27Ua3w5p4OuvqPAmCLoDy6WA9+VjUvNr6Thyyk0LdWk+ZRRX6YIqAPRcysftCJhzkMqyZEg2FXIZ+YZ44kZyHU6n5mDpQ2lnGRLbE6OAJAIKDg1ucGE7Gc1DI4eHsiMJyNfJKqxg82QhBEPQ9T0NY30kSYq2npJxSXM0vZ/BUo7hCjRXfXAAAPDSyC3oGugMA+nfyAgDEpRRI1TQim2R0n/b27dvx+eefN9j++eef48MPPzRJo4iLA9uilNwyZBVVQqmQ6/8okeUx76mht/bEI7OoAmE+LnhybDf99n6dPAEAl7OLUVTBorxELWV08LRmzZpG6yz5+fnhtddeM0mjiIsD2yKx1ykm1AMqR+Y7SYUz7gzFpebjo2MpAIDX7o42eG36uakQ4uUMQQDOpBVI1EIi22N08JSamorOnTs32B4WFobU1FSTNIoALxcu0WJrWKLAOgR7sudJpNZosfTLcxAE4J7+IRjeteEXXw7dERnP6ODJz88PZ8+ebbD9zJkz8PHhHw1T8XbVJY3nlVZK3BJqCV2+E5PFrUFtoUz2PP3n178Rn1UMb1clXryzZ6P79K8ZumOxTKKWMzp4uv/++/Hkk0/iwIED0Gg00Gg02L9/P5566ilMnz7dHG1sl7g4sG25ml+O9IJyOMhl6B/mKXVz2jXmPOkk5ZTi7X0JAIBld/XUpwLU1z9M1/PEYplELWf0bLuXX34ZycnJGDt2LBwcdIdrtVrMnDmTOU8mJC7Rwpwn23CsZj27PiEecFG2ahIrmYjY85RR2H5rPQmCgBe/Ooeqai1GdPPF5L7BTe7bM9AdKkc5i2USGcHoT3mlUomdO3filVdewenTp+Hs7Izo6GiEhYWZo33tFmfb2Rb9kB3znSTn5+YER4UMao3Qbms9fRmXjt+v5ELlKMerk6Mhk8ma3NdRIUefYF29p7gUFsskaolWfyXr1q0b7rvvPowfPx75+fnIz+d4uSlxtp1t0dd36sz6TlKTy2XtOmk8t6QSr/zwFwDg6dju6ORz4+CxX81QM/OeiFrG6ODp6aefxtatWwEAGo0Go0aNQv/+/REaGnrDiuDUcmLOU24Jgydrd62gHGl55VDIZRgYzuDJGtQmjbe/4OmVHy6ioEyNnoHumHdzw5nRjRFn3J1KLTBjy4jsh9HB0xdffKFfAPi7777D33//jUuXLuGZZ57Biy++aPIGtlfMebIdYq9T72APdHBivpM1aK+1nn69fB1fnUqHTAa8PiUaji3M9xKDJxbLJGoZo4OnnJwcBAQEAAB+/PFHTJ06Fd27d8fcuXNx7tw5kzewvRJ7nsqqNKhQayRuDTVHX9+JQ3ZWoz3OuCuv0uDFr3WfwbOHhSMm1LPFx3Z0c0KoN4tlErWU0cGTv78//vrrL2g0GuzevRu33norAKCsrAwKBasqm4q7ygEOcl2SJ3ufrJs4047r2VmP9ljraf2+y0jLK0eQhwrP3tbD6OP7hbJYJlFLGR08zZkzB1OnTkXv3r0hk8kQGxsLADh+/DgiIyNN3sD2SiaT1an1xODJWmUVVSA5twxyGZjvZEXEnqf0gvbR83Q+vRAf/JYEAFg9qXerho9ZLJOo5Yx+h61cuRK9e/dGWloa7rvvPjg5OQEAFAoFXnjhBZM3sD3zdlHienElgycrJvY6RQW5w13lKHFrSKSv9VRg/7WeNFoBS3edg0Yr4M7oQMRG+bfqPPWLZcrlTZc3IGrvWpXdeu+99zbYNmvWrDY3hgx56ZdoYfBkrbgki3WqW+spq7hSX7rAHu34PRnn0gvhpnLAiglRrT4Pi2UStZz9fh2zA/paTwyerNbxv1nfyRoZ1HrKs9+8p6v5ZfjXz/EAgKXje8LPXdXqc4nFMgHmPRHdCIMnK6avMl7GqcPW6HpxJa5cL4VMBgxm8GR17L3WkyAIWP7NBZRVaTAo3AvTB4W2+ZwslknUMgyerJi+1hN7nqzSiZohu8gAd3i6NL7oKknH3quM/3AuA/svZcNRIcOaKdEmyVES6z0xeCJqHoMnK8bZdtbtGIfsrJo9F8osLFNj5be6JVgeG93VZPlJYvCUkF3CYplEzWhV8HTlyhW89NJLuP/++5GdnQ0A+Omnn3DhwgWTNq694+LA1k2sLH4T6ztZpRBv++15en33ReSUVCKioyseGxNhsvOyWCZRyxgdPB06dAjR0dE4fvw4du3ahZKSEgDAmTNnsGLFCpM3sD3j4sDWK6+0CpezdK/9wZxpZ5X0OU8F9tXzdPzvXPzvRBoAYM2UPnByMG1xYv3QHZPGiZpkdPD0wgsv4JVXXsEvv/wCpbI2z+OWW27BsWPHTNq49s7LhT1P1upETa9Td/8O+iCXrIs4bCfWerIHldUaLP1KtwTL/YM7mWWiQr+aZV2Y90TUNKODp3PnzuHuu+9usN3Pzw85OTkmaRTp1O15EgRB4tZQXcf+Zn0na+fnpoKjQoZqra7Wkz1478AV/H29FB3dnPDCePOs6FC/WCYRNWR08OTp6YmMjIwG20+dOoXg4GCTNIp0xJ4ntUZAcWW1xK2husTimDd1YfBkrRRyGYLsqNZTYnYx3juYCABYOaEXPJzNU9G+frFMImrI6OBp+vTpeP7555GZmQmZTAatVosjR45g8eLFmDlzpjna2G45KxVwdtTlM7BcgfUoKKvCpcwiAKzvZO1qZ9zZdtK4tmYJFrVGwNhIP9wRHWC2x2KxTKIbMzp4eu211xAZGYnQ0FCUlJQgKioKI0eOxLBhw/DSSy+Zo43tGmfcWZ8TSXkQBCCioys6ujlJ3RxqRoinfRTK/OyPNPyRnA8XpQKrJ+sWZTcnFsskap7Ra9splUps2bIFy5cvx7lz51BSUoJ+/fqhW7du5mhfu+ftqkR6QTln3FkR/Xp2HLKzemLPU7oNz7jLLqrAmp8uAgAW39bDIuv0sVgmUfNatTAwAISGhiI0tO3LAVDzxEKZuSUMnqyFWN+JxTGtnz3Uelr13V8orqhGnxAPzBoWbpHHrF8s011lnvwqIltl9LDdPffcgzfeeKPB9rVr1+K+++4zSaOolreL7kOLPU/WoahCjb+u6fKdmCxu/Wx9fbu9f2Xhh3MZUMh1S7AoTLAES0vULZZ5OrXAIo9JZEuMDp5+/fVX3HHHHQ22jx8/Hr/++qtJGkW1apdo4VIJ1uDP5DxoBSDcxwX+bVjBnixDHLa7VlAOjY1Nuy+prMbyb84DAOaP6IxeQR4WfXyx9+kUgyeiBowOnkpKSgyKY4ocHR1RVFRkkkZRLR9XLg5sTY6zvpNNMaj1VFQhdXOM8q+f43GtsAKh3s54emx3iz8+856ImmZ08BQdHY2dO3c22P7ZZ58hKirKJI2iWvqeJw7bWYVjYn2nCOY72QKDWk82NHR3Oq0AO35PBgC8OjkazkrTLsHSErU9TyyWSVSf0Qnjy5Ytw5QpU3DlyhXccsstAIB9+/bhf//7Hz7//HOTN7C98+YSLVajpLIa59MLAbDnyZYEezojJbcMV/PLbKIul1qjxdJd5yAIwN39gjGye0dJ2hEZ6GZQLLOrn5sk7SCyRkb3PE2YMAFff/01EhMT8dhjj+HZZ5/F1atXsXfvXkyePNkMTWzfvDhsZzX+TM6DRisg1NtZ35tB1s/WCmVuPZyEixlF8HRxxEt39pSsHSyWSdS0VpUquPPOO3HnnXeaui3UCG8O21kNfX0n9jrZlNoZd9Zf6ym7qALr914GALx0ZxR8OkhbhLVfmCdOJOchLjUfUwexNA2RqNV1nqqqqpCdnQ2t1nC18k6dOrW5UVRLDJ4Ky9Wo1mjhoDC6s5BM5PjfrO9ki2yp5+no37moUGsRGeCGe/pLv1Yok8aJGmd08JSQkIC5c+fi999/N9guCAJkMhk0Go3JGkeAZ83in4KgC6Ck/ibaXpVVVePsVV2+E+s72RZbqvUk5tQNCvc2+xIsLcFimUSNMzp4mj17NhwcHPD9998jMDDQKt7g9sxBIYeHsyMKy9XIK61i8CSRuJQCVGsFBHmo9D0ZZBvq13qyVKHJ1jhXEzxFB1u2plNTxGKZaXnlOJ1aIFnyOpG1MTp4On36NE6ePInIyEhztIca4e2q1AdPJA1xSZabuvjwC4ON8XdXwUFeW+vJWpP9tVoBF9J1tfJ6W0nwBOh6n9LyyhGXms/giaiG0Qk0UVFRyMnJMUdbqAleXKJFcsfEfKcuzHeyNbZS6yk1rwzFldVQOsjRzb+D1M3Rq817KpC2IURWxOjg6Y033sBzzz2HgwcPIjc3F0VFRQY3Mj1vV91QHZdokUaFWoMzaazvZMvEobv0AuudcScO2fUMdIejFU0MEYOn0yyWSaRn9LBdbGwsAGDs2LEG25kwbj7erux5klJcaj6qNFr4uzshzMdF6uZQK+hn3OVZb8/TeX2+k7vELTHEYplEDRkdPB04cMAc7aBmiIUyc0sYPEmh7np2zHeyTbYw407seept4QWAb8RRIUefEE+cSMpDXEoBgycitGLYbtSoUc3ejLVx40aEh4dDpVJhyJAhOHHiRIuO++yzzyCTyRqtan7x4kVMnDgRHh4ecHV1xaBBg5Cammp026yFuEQLe56kISaLM9/Jdul7nqx02E4QBH3PkzUli4v6dfIEwHpPRKJWDaz/9ttvePDBBzFs2DCkp6cDAD7++GMcPnzYqPPs3LkTixYtwooVKxAXF4eYmBiMGzcO2dnZzR6XnJyMxYsXY8SIEQ3uu3LlCm6++WZERkbi4MGDOHv2LJYtWwaVSmVU26yJvso4Z9tZXGW1BqdqEmWZ72S7rL3nKS2vHEUV1VAq5Ojub309OyyWSWTI6ODpyy+/xLhx4+Ds7Iy4uDhUVlYCAAoLC/Haa68Zda5169ZhwYIFmDNnDqKiorB582a4uLhg27ZtTR6j0WjwwAMPYNWqVejSpUuD+1988UXccccdWLt2Lfr164eIiAhMnDgRfn5+xl2oFRGDJ/Y8Wd6ZtEJUVmvh28EJER1dpW4OtVL9Wk/WRhyyiwx0g9LBepLFRfWLZRK1d0a/S1955RVs3rwZW7ZsgaNjbbXZ4cOHIy4ursXnqaqqwsmTJ/UJ6AAgl8sRGxuLo0ePNnnc6tWr4efnh3nz5jW4T6vV4ocffkD37t0xbtw4+Pn5YciQIfj666+bbUtlZaVVzxpkzpN06pYoYL6T7RJrPak1ArKLK6RuTgPnrHjIDqgtlikIwGmWLCAyPniKj4/HyJEjG2z38PBAQUFBi8+Tk5MDjUYDf39/g+3+/v7IzMxs9JjDhw9j69at2LJlS6P3Z2dno6SkBK+//jpuv/12/Pzzz7j77rsxZcoUHDp0qMm2rFmzBh4eHvpbaKh1LYDJnCfp6Itjcj07m2bttZ7OW1ll8cZw6I6oltHBU0BAABITExtsP3z4cKPDaKZSXFyMGTNmYMuWLfD19W10H3GR4kmTJuGZZ55B37598cILL+Cuu+7C5s2bmzz30qVLUVhYqL+lpaWZ5RpaS+x5KqvSoELNUhCWUlWtxckU3R+KIVzPzuYF64Mn60oaFwTBamfa1WULxTI/O5GK4a/vx54LjX8BJzIVo0sVLFiwAE899RS2bdsGmUyGa9eu4ejRo1i8eDGWLVvW4vP4+vpCoVAgKyvLYHtWVhYCAgIa7H/lyhUkJydjwoQJ+m1isOTg4ID4+HiEhobCwcEBUVFRBsf27Nmz2WR2JycnODlZ75px7ioH/fIS+WVVCPSwzuUl7M259AJUqLXwdlWim5/1VHym1rHWWk9X88tRWK6Go0KG7gHW+zoTg6dTNcUy5Va2RmBVtRZv/XwZOSWVeOzTOKyf1hcTYoKkbhbZKaODpxdeeAFarRZjx45FWVkZRo4cCScnJyxevBhPPPFEi8+jVCoxYMAA7Nu3T19uQKvVYt++fVi4cGGD/SMjI3Hu3DmDbS+99BKKi4vx9ttvIzQ0FEqlEoMGDUJ8fLzBfpcvX0ZYWJixl2o1ZDIZvFyVuF5cibxSBk+WcqymvtNgK1nhntrGWmfciUN2PQLc4OSgkLg1TROLZRZXVOPK9RJ0s7JZgbsvZCKnpBIyGaDRCnjqs1NQa7SY0j9E6qaRHTIqeNJoNDhy5Agef/xxLFmyBImJiSgpKUFUVBQ6dDD+G9OiRYswa9YsDBw4EIMHD8b69etRWlqKOXPmAABmzpyJ4OBgrFmzBiqVCr179zY43tPTEwAMti9ZsgTTpk3DyJEjMWbMGOzevRvfffcdDh48aHT7rIm3S23wRJZxPKmmOCbrO9kFa631dM4G8p0Aw2KZp1ILrC54+vhoMgDgiTFdkV1cic/+SMOzn59BVbUW0wd3krZxZHeMCp4UCgVuu+02XLx4EZ6eng2Gx4w1bdo0XL9+HcuXL0dmZib69u2L3bt365PIU1NTIZcbl5Z19913Y/PmzVizZg2efPJJ9OjRA19++SVuvvnmNrVVal41S7QweLIMtUaLk8m64Okm5jvZBX3wZGU9T9Y+066u/p28dJXGU/MxdZD1TKy5mFGEP5Lz4SCX4YGbwtCxgxOcHOT48GgKXth1DlUaLWYODZe6mWRHjB626927N/7++2907tzZJA1YuHBho8N0AG7YW7Rjx45Gt8+dOxdz585tY8usi77WE4MnizifXojSKg08XRzRw8q+YVPrhHjrhu2uFZRbTc5O3cri1t7zBFhvpfGPjqYAAMb1CoC/u64g8sqJvaB0kGPLb0lY/s0FVKq1WDDSfJOaqH1pVZ2nxYsX4/vvv0dGRoZV10eyJ/oq42UsUGcJ4pDdoHBvq/gjS23n7+ZUp9ZTpdTNAQCkF5Qjv0wNB7nMKiuL12eNxTILy9X4+pRupYsZQ2tzW2UyGf55R08sHNMVAPDqjxfx7v4ESdpI9sfonqc77rgDADBx4kSDJFpBECCTyaDRcCq9OehrPbHnySKOi8UxWd/Jbjgo5Aj0VCEtrxxX88sQ4CH9kk1ir1N3fzeoHK03WVwkFstMyyvH6dQCjOzeUeom4YuTV1Gu1qC7f4cG71eZTIbF43rAyUGOf/1yGW/9fBmV1VosurU7J4FQmxgdPB04cMAc7aAb8OL6dhaj0Qr4M1k3LMF8J/sS4ulSEzyVY2C41K2xnWTxuvp38kJaXjniUvMlD560WgGfHNMN2c0cGt5kQPTE2G5QOsix5qdL2LA/EVXVWrwwPpIBFLWa0cHTqFGjzNEOugEuDmw5Z64WoLiyGm4qB/QMdJe6OWRCtUnj1jHj7ny6LtWhd4htBU/fnL5mFcUyDyfmICmnFG5ODri7X3Cz+z48KgJODnKs/O4vvP/r36is1mLFhCgGUNQqrVqB8rfffsODDz6IYcOGIT1dN9b88ccfN1uIktrGi0u0WMSlzCI8+slJAMDNXX2hYL6TXbGmWk+2liwuql8sU0piovg9A0Lg6nTjvoDZwzvjtbujIZMBO35Pxj+/Oi/5NZBtMjp4+vLLLzFu3Dg4OzsjLi4OlZW6xMvCwkK89tprJm8g6bDnyfxOJOXhvs1HkVVUie7+HbB8QttKcZD1saZyBRmFFcgtrYJCLkNkgPUni4vqF8uUytX8Muy/pFuh4sGbWl4E+R9DOuHNe2MglwH/O5GKJV+chYYBFBmpVbPtNm/ejC1btsDR0VG/ffjw4YiLizNp46iWvlRBWRUEgW90U9tzIRMPbj2O4opqDAzzwucPD2MldztkTcN2Yr5TN78ONpEsLhKLZQLSliz49HgqtAIwvKsPuhq5fNK9A0Kwfno/KOQyfBl3FU/vPA21RmumlpI9Mjp4io+Px8iRIxts9/DwQEFBgSnaRI0Qh+3UGgHFldUSt8a+/Pd4Kh795CSqqrWI7emPT+YPgYeL440PJJsj1npKr6n1JCVbHLIT1Q7dFUjy+BVqDT47kQoAmHFTeKvOMTEmCBv/0R+OChm+O3MNC/8bh6pqBlDmpNUKKCxTIymnFCdT8pGWJ/2XmNYyOmE8ICAAiYmJCA8PN9h++PBhdOnCAmTm4qxUwNlRgXK1BvmlVXBX8Y97WwmCgHf2JeLfey8DAKYPCsUrk3vDQdGqVECyAfVrPUlZrkA/086GksVF/SUulvnD2Qzkl6kR5KFCbE+/Vp/n9t4BeH/GADzySRz2XMjCI5+cxHsP9LepnkCpCIKA0ird36O80irklVXp/59fVoW8UrXu55rt+WVVyC9TGwyRPjW2G565tbuEV9F6RgdPCxYswFNPPYVt27ZBJpPh2rVrOHr0KBYvXoxly5aZo41Uw9tVifSCcuSVViHMx1Xq5tg0jVbAim/P45Njum+vT9zSlbVf2gEHhRwBHipczZe21lPdZHFbWJalvn71imVa+svcRzXlCR64KazNX3ZuifTHBzMH4qGP/8T+S9lY8NGf+M+MgXBWtr8AqrBcjbS8sjoBUFWdAEhtuL2sCmpN63pv3Zwc4OWqtOnn2Ojg6YUXXoBWq8XYsWNRVlaGkSNHwsnJCYsXL8YTTzxhjjZSDTF44oy7tqlQa/DMztP46XwmZDJg1cReXPeqHQnxcq4JnqSr9ZRVVImcEl2yeJQNlsOQsljm2asFOJNWAKVCjmkmWl9vZPeO2D57MOZ9+Ad+S8jB7O0nsG32oBbN4LN1ldUaHLiUjV1x6TgQn210QOTsqIC3qxJero7wclHq/i/+66qEt4vuPu+a/3u6KKF0sP3efaNfGTKZDC+++CKWLFmCxMRElJSUICoqCh06GJewR8arLZRpHcsi2KLCcjUe+uhPHE/Kg1Ihx/rpfXFHdKDUzSIL0pUryJM0adxWk8XrkqpYplie4I7oAPh2cDLZeYdG+ODjeYMxe9sfOJ6Uh5nbTmD7nEF2mSIhCAJOpRVgV9xVfHcmA4XltX9T/Nyc4NPBCd6NBEOeLo6GwZGLbfcetUWrw2qlUomoKE7ltiTvmiTmvFLrWJfL1mQVVWDWthO4lFkMNycHvD9zAIZF+ErdLLIwayhXIAZPvYJsb8hOJEWxzPzSKnx75hoAYIYZeosHhHnjk/lDMGPrcZxMyceDHxzHR3MHw7Nmwo6tS8srw9en0rHrVDqSckr12/3dnTC5XzDu6R9iE2ssWgOjg6fS0lK8/vrr2LdvH7Kzs6HVGs5O+Pvvv03WODLEnqfW+/t6CWZsPYH0gnJ0dHPCjjmDbPoPF7WeNRTKrJ1pZ3tDdqL6xTItsYD2zj/TUFWtRe9gd33SuqnFhHrifw/dhAc/OI6zVwtx/5bj+GTeYPiYsJfLkoor1PjpfCa+PHlVv+A5oBtuu713AKb0D8awCBYENpbRwdP8+fNx6NAhzJgxA4GBgUywtSAfVy4O3Bqn0wowd8cfyCutQriPCz6eNwShNVPWqf0Re57SC6TvebLFmXai+sUyu5m5x0JTdx27m5pex84UegV54LOHhuKBD47jYkYRpv/nGD5dMAR+btIvJt0S1RotjlzJxa64q9hzIRMVal0nh0wGDO3igyn9Q3B77wB0aAc5XeZi9DP3008/4YcffsDw4cPN0R5qhr7niQnjLXYwPhuPfhKHcrUGfUI8sG32IJPmSZDt0QdP+eUW6zGpK7uoAteLKyGXAVGBths8icUyTyTlIS413+zB08H4bFzNL4eHsyMmxASZ9bEAoEeAG3Y+fBMe2HIcCdklmP6+LoCy5uK5FzOKsCvuKr45fQ3ZxbXpHREdXTGlfwgm9wtGsKf1tt+WGB08eXl5wdvb2xxtoRvwduESLcbYFXcVz31xFtVaASO6+WLzgwPaxewZal6AuwoKuQxVGi2ul1TC392yvQlir1NXvw42n2zbv5OXLnhKKcC0QZ3M+lhiovjUgSEWe94iOnbA/z08FPdvOYa/c0ox9f2j+O/8m6yq5zq7uALfnr6GL+PScTGjSL/dy8URE2OCMKV/CPqEeHCUyMSM/kvy8ssvY/ny5fjwww/h4mI9L6D2wIvDdi32n1+v4LUfLwEAJvcNwtp7Y+xieiy1nYNCjsA6tZ6kCp5ssb5TfZYqlpmcU4pDl69DJjNuHTtT6OTjouuB+uA4UnLLdEN484cg3Fe6WnsVag1+/isLu+Ku4reEHH3hSUeFDGMj/TGlfzBG9/DjZ54ZtSh46tevn0HUmpiYCH9/f4SHhxusbweA69uZkTeH7W5IqxWw5qeL2PJbEgBg/s2d8c87elp8aIasW91aTwMs+7e4tjimHUxY6B9WWyyzsFwND2fzTOsXc51Gde8oSYHgEC8X7HxoKP7xwTH8fb0U0/5zFJ/Ov8noNfXaQqsV8GdKPr48eRU/nsswWKarXydPTOkfgruiA/Vfssm8WhQ8TZ482czNoJYQg6fCcjWqNVouI1KPWqPFc1+cxVen0gEA/7wjEg+NjJC4VWSNams9WT5p3B6SxUW+HZzQydsFqXllOJNmnmKZ5VUa/N+faQCAWRIWsw3wUGHnQ0Px4AfHEZ9VjOn/OYpXJkfD1UkBraCrnSQIgAABWi0gANCK2wTB4GdtzeLu4v/1/6L2PFrxXAKQVViBb86kIy2v9vUa7OmMKf2DcXe/YHTpyDqLltai4GnFihXmbge1gGfNtzpB0AVQtjp11hxKK6vx6Kdx+PXydTjIZVh7bx9M6R8idbPIStXWerJsoczs4gpkFVVCJoNNVhZvTP9OnkjNKzNbscxvTqejqKIanbxdMMqCxTgb09HNSV/G4K+MIjzyyUmLPn4HJwfcER2AKf1DMDjcmz3qEmp19uzJkydx8eJFAECvXr3Qr18/kzWKGuegkMPD2RGF5bo1hhg86eSWVGLujj9w5mohnB0V2PRgf4zu0frFQsn+SVXr6UK6LqE3omMHu5m80K+TF742U7FMQRD0ieIP3tTJKoIFb1cl/rfgJiz75jwuZxVDJpNBBkAuB2SQQSZD7baa/8tlde8D5DKZ/l+g9mdZ3f/rzyGDylGOMZF+uC0qwOYnGdgLo9+92dnZmD59Og4ePAhPT08AQEFBAcaMGYPPPvsMHTtK+83A3nm7KvXBE+kq5s7cdgJJOaXwcnHEttmD9IuWEjVFqirj+iE7O0gWF5mzWGZcaj7+yiiCk4McUweaZh07U/BwccQ797PDoD0zOmnmiSeeQHFxMS5cuIC8vDzk5eXh/PnzKCoqwpNPPmmONlIdXjVLtHBxYOCva0WYsul3JOWUItjTGV88OoyBE7VI/VpPlmJPM+1E9YtlmpLY6zQxJshulkgh+2B08LR7926899576Nmzp35bVFQUNm7ciJ9++smkjaOGvF11Q3XtfYmWo1dyMe39o7heXInIADfsemwYIpg0SS1Uv9aTpdTOtLOPfCegtlgmYNqSBdeLK/HjuQwAwEwJE8WJGmN08KTVahuUJwAAR0fHBuvckel5u7Ln6adzGZi17QSKK6sxuLM3dj481OK1esi2OSjkCKh5zVgqaTynpBIZhRWQyYBedtTzBNQO3cWlFJjsnJ+dSIVaI6BfJ0+7mJlI9sXo4OmWW27BU089hWvXrum3paen45lnnsHYsWNN2jhqSKzhkVvSPoOnj4+l4LH/xqFKo8XtvQLw0dzBZqstQ/bN0nlP4pBdZ19Xu1tTzNTFMqs1Wvz3RCoAYOZQCxfiImoBo9/B7777LiZOnIjw8HCEhuoS+NLS0tC7d2988sknJm8gGRKXaLH3nidBEJBTUoWU3FKk5JYhJbcUlzKL8fNfWQCAB4Z0wupJvbkSOLVaiJcLjidZrtbT+av2lywuMnWxzL0Xs5BRWAEfVyXuiA40RROJTMro4Ck0NBRxcXHYu3cvLl3SLX/Rs2dPxMbGmrxx1JB+cWA7mG2n1QrIKKpASk4pUvLKkJxbitTcMiTnliE1txSlVZpGj3smtjueHNuVazVRm1i65+n8NfsNnkxdLFNMFJ82KBRODpyaT9anVX3HMpkMt956K2699VZTt4duwMfVtnqe1BotruaX1wmMav9Nyy9HVXXTeXIyGRDk4YwwHxeE+bgizMcFA8K8MCicC1NT21m6UOb5mhpP9jTTri5TFctMzC7G71dyIZcBD1h4HTuilmpx8LR//34sXLgQx44dg7u74UyRwsJCDBs2DJs3b8aIESNM3kiqZY09TxVqDVLqBUapNT1J6fnlaG4muKNChlAvF3TycUG4jys6ebsg3NcFnbxdEertzG+dZDZiocx0C/Q85ZVWIb1A9zhRdjTTrq7+YaYplvlxTa/T2J7+CPZ0NkHLiEyvxcHT+vXrsWDBggaBEwB4eHjg4Ycfxrp16xg8mZmY82QtwdPJlDw88MFxVKib7kFSOcrrBEY1/9b0JAV6qLhGH0lC3/NUUG7y4o711U0Wd1fZ5wSHfqFtL5ZZUlmNL+N0a1NKuY4d0Y20OHg6c+YM3njjjSbvv+222/DWW2+ZpFHUNLHnqaxKgwq1BipHaXtmvj+bgQq1Fq5KBSL8OugDI7EnKdzHBR3dnJifRFYn0KOm1lO1FjkllfAzY7mL83ZYHLO++sUyu/m7GX2Or+KuoqSyGl06umJ4Vx8ztJLINFocPGVlZTVa30l/IgcHXL9+3SSNoqa5qxzgIJehWisgv6wKgR7SdmufTNFNTX5tSjQm9Q2WtC1ExhBrPaUXlCMtv9yswdM5/Uw7+xyyA2qLZZ5IykNcar7RwVPddexm3BTGL1xk1Vo8XhIcHIzz5883ef/Zs2cRGMgppeYmk8msJu+prKoaF67pkmAHMombbJClksbFmXb23PMEtK1Y5rG/85CQXQIXpQL3DAgxccuITKvFwdMdd9yBZcuWoaKiosF95eXlWLFiBe666y6TNo4ap6/1JPESLWfSCqHRCghwVyHIgxW+yfaISePmLFeQX1qlP3+vIHsPnjwBtK5Y5sfHkgEAk/sF221eGNmPFg/bvfTSS9i1axe6d++OhQsXokePHgCAS5cuYePGjdBoNHjxxRfN1lCq5VWzREtuqeXW5GrMyZQ8AMCAcC92sZNNskStJ7HXKczHxe6r4be2WGZmYQX2XNAVwGVFcbIFLQ6e/P398fvvv+PRRx/F0qVLIQi6+ecymQzjxo3Dxo0b4e/vb7aGUi1vsdaTxMN2Yr7TgJqueiJbY4lhu3PtIFlcVLdY5um0AoxqYb2n/x5PgUYrYHBnb0QG2G9eGNkPo4pkhoWF4ccff0R+fj4SExMhCAK6desGLy/+8bQkMXjKK5Nu2E6rFfTB08Bw/v7JNlmi1pM4084eK4s3Rl8sMyW/RcFTVbUW/z2RBoC9TmQ7WlVh3MvLC4MGDTJ1W6iFanOepOt5unK9BEUV1XB2VKBnIL8pkm2yRK2nc+0teKoplnkqraBF++++kKkrFeHmhHG9AszbOCITYXVCG2QNs+3+rOl1ign1gCOLXJKNCvBQQS6DvtaTqRWWqZGWp+vV6m3nyeIiccadWCzzRj4+mgwAuH9wJ36WkM3gK9UGeVtD8JRcM2QXxhIFZLscFXJ9rbQ0Mwzdicninbxd4OFi38niosgAw2KZzbmYUYQ/kvPhIJfhH0M6WaiFRG3H4MkGeblIvziwOBV5QBjznci2BdcM3Ylrz5lSbbJ4+xnadqgplgncuGSBWBRzXK8A+JuxSCmRqTF4skFS9zzllFQiKacUQG0XPZGtMueMu/Y0066ulhTLLCxX4+tTunXsmChOtobBkw3Slyooq9KXjLCkuJp8p25+HdrNUATZL3MWymxvM+1ELSmW+cXJqyhXa9DD3w2DO3P4n2yLVQRPGzduRHh4OFQqFYYMGYITJ0606LjPPvsMMpkMkydPbnKfRx55BDKZDOvXrzdNY62AOGyn1ggorqy2+OOzRAHZE3MVyiwsVyMlV9eb1V6SxUX1i2XWp9UK+ORYzTp2Q7mOHdkeyYOnnTt3YtGiRVixYgXi4uIQExODcePGITs7u9njkpOTsXjxYowYMaLJfb766iscO3YMQUFBpm62pJyVCjg7KgBIU65AXxyTyeJkB8w1bHehJlk8xMtZP0O2vRCLZQLA6UZKFhxOzEFSTincnBxwdz8uKE62R/Lgad26dViwYAHmzJmDqKgobN68GS4uLti2bVuTx2g0GjzwwANYtWoVunTp0ug+6enpeOKJJ/Dpp5/C0dH+hpakynuqrNbgbM1QBJPFyR6E1imUacph8PY6ZCfSD92lNBy6ExPF7xkQAlenVpUbJJKUpMFTVVUVTp48idjYWP02uVyO2NhYHD16tMnjVq9eDT8/P8ybN6/R+7VaLWbMmIElS5agV69eN2xHZWUlioqKDG7Wrm7ekyWdTy9EVbUWPq5KhPu4WPSxicxBrPVUWa3FdRPWejqXrvscaW/J4iJx6K5+3lNaXhn2X9KtY/fgTUwUJ9skafCUk5MDjUbTYE08f39/ZGZmNnrM4cOHsXXrVmzZsqXJ877xxhtwcHDAk08+2aJ2rFmzBh4eHvpbaGhoyy9CIrWFMi27RIs4ZNc/jIsBk32oW+vJlHlP59vpTDuROOPudFqBQbHMT4+nQisAN3f1RVe/DlI1j6hNJB+2M0ZxcTFmzJiBLVu2wNfXt9F9Tp48ibfffhs7duxo8R/3pUuXorCwUH9LS0szZbPNwrtmllteqemrIjentjgmh+zIfgSbOGm8qEKtL+fRXoftIgPc4OyoMCiWWaHWYOcfqQB0ieJEtkrSwWZfX18oFApkZWUZbM/KykJAQMM1jq5cuYLk5GRMmDBBv02r1QIAHBwcEB8fj99++w3Z2dno1Km2Wq1Go8Gzzz6L9evXIzk5ucF5nZyc4OTkZKKrsgwpep4EQdB3wXOmHdmTEC9nnEgyXdL4hZohu2BPZ/0Qe3ujK5bpgeNJeYhLzUc3fzf8cDYD+WVqBHmoMDbST+omErWapD1PSqUSAwYMwL59+/TbtFot9u3bh6FDhzbYPzIyEufOncPp06f1t4kTJ2LMmDE4ffo0QkNDMWPGDJw9e9Zgn6CgICxZsgR79uyx5OWZlRSLA6fkliGnpApKhRy92tnUa7Jvpq71dL4dVhZvTL96xTI/qilP8MBNYXDgOnZkwySf5rBo0SLMmjULAwcOxODBg7F+/XqUlpZizpw5AICZM2ciODgYa9asgUqlQu/evQ2O9/T0BAD9dh8fH/j4+Bjs4+joiICAAPTo0cP8F2Qh3h1qep4smDAu5jtFh3hAVVMqgcgemLrWk7imXXsdshPVLZZ59moBzqQVQKmQY9og688rJWqO5MHTtGnTcP36dSxfvhyZmZno27cvdu/erU8iT01NhVzObyj1SdHz9GcK17Mj+2TqWk/isiy92nvwVKdY5rv7EwEAd0QHwLeDbaVJENUnefAEAAsXLsTChQsbve/gwYPNHrtjx44bnr+xPCdb5yVBnaeTKXkAGDyR/QnxNKz11JaZpCWV1e0+WVwkFstMzSvDz3/pcltnDguXtlFEJsAuHRulL5JpoWG7wnI1LmfpZsxwMWCyN3VrPeWUtO09dSG9EIIABHqo2MOC2qE7QJcD1i/Us8l9iWwFgycbJa5vV1iuRrVGa/bHE2fZhfu4oKMb/yCQfVE6yBHgrgLQ9qG7c+28vlN9/ev0VM+8KZz14cguMHiyUV41dZ4EAY0uvGlqcVzPjuycqWbctfdlWeob0lk3gcfLxRETYuxrnVFqv6wi54mM56CQw8PZEYXlauSXVcHHzMMDYnFM5juRvQrxcsaJZBMET9d0NZ4YPOn0CHDD9jmDEOCugrOSs3TJPrDnyYaJeU+5bczRuJFqjVa/MjqLY5K9MsWMu9LK2mravdp5jae6xvTwQ89APh9kPxg82TBx6M7ciwNfzChGuVoDd5UDunbkWlRkn0wxbPdXRhEEAfB3d4Kfm8pUTSMiK8PgyYZ5u+qG6sy9RMufNSUK+od5QS5nsifZJ1P0PJ27ynwnovaAwZMN83a1TM+TWFl8AEsUkB2r2/MkCEKrznGeM+2I2gUGTzbMy0I5T/rgiflOZMdMUevpHGfaEbULDJ5smH6JFjP2PF0rKEdGYQUUchn6srgd2bG21noqq6pNFmfwRGTfGDzZMEss0SKuZxcV6A4XJStbkH1rS9L4xYwiaAWgo5sT/NyZLE5kzxg82TAfV/P3PMVxMWBqR2qTxo0PnpgsTtR+MHiyYZbpeeJiwNR+tGXG3bl0XXFMJosT2T8GTzZMzHkyV/BUWlmNixnFAFgck9qHtgzbcVkWovaDwZMNE3ueyqo0qFBrTH7+M2kF0GgFBHmoEOjhbPLzE1mb4Jqep/QC44Kn8ioNErJ1XzQYPBHZPwZPNsxd5QCHmqKV5sh7+lNfooCLAVP7UHfYzphaTxczdcnivh2c4O9u3nUmiUh6DJ5smEwmM2vek1jfaSDznaidCPRwhkwGVKi1yDXiPVVbHNMdMhmr8BPZOwZPNk5f68nES7RotQLiUjnTjtoXw1pPLR+640w7ovaFwZON86pZoiW3tNKk503ILkFxRTVclApEBriZ9NxE1qw1M+7OcVkWonaFwZON8xZrPZl42E4sUdA31BMOCr5MqP0wdsZdhVqDhGxWFidqT/hX0cZ5ieUKykw7bHcymflO1D4Z2/N0MaMIGq0AH1clAj1YWZyoPWDwZON8zNTzdLIm36k/gydqZ4ytMn6+zpAdk8WJ2gcGTzZOP9vOhKUKrhdXIiW3DDIZgydqf4wdtjuvryzubrY2EZF1YfBk48Scp7wS0wVPYomCHv5ucFc5muy8RLbA2FpP51hZnKjdYfBk48ScJ1MWyTxZkyzOXidqj4yp9VSh1uBylq6yOGfaEbUfDJ5snLcZimSyOCa1Z8bUeorPLEa1VoCXiyOCPbmEEVF7weDJxulLFZRVGbWcRFMq1Bp9DgeLY1J71dIZd+eYLE7ULjF4snHisJ1aI6CksrrN5zuXXogqjRa+HZzQydulzecjskUtTRo/z3wnonaJwZONc1Yq4OyoAGCaobu6Q3b8Jk3tldjzlH6j4OkaK4sTtUcMnuyAKfOe/kzmenZEYv5Sc8N2ldUaxGfqksXZ80TUvjB4sgN1857aQhDqLAYczuCJ2q+WDNtdziyBWiPAw9lR31NFRO0Dgyc7oC+UWdq2JVqSckqRV1oFpYMcvYJY8I/ar7pVxpuaiFG3vhOHuInaFwZPdsDbRVfIMq+0sk3nEfOdYkI84OSgaHO7iGxVoKcKMhlQrtY0ORxed6YdEbUvDJ7sgKl6nsTgicUxqb1zclDA3635Wk+caUfUfjF4sgPeLqZZHPhP/Uw77za3icjWNbdAcFW1Vp8szjXtiNofBk92wLtD2xcHLiirQmJ2CQDOtCMCmi+UeTmrGFUaLdxVDqyHRtQOMXiyA6boeRJn2XXxddXP3iNqz5qbcXeelcWJ2jUGT3bAywR1nsR8J/Y6Eek01/N0jvlORO0agyc7oC+S2YZhOxbHJDLU0p4nImp/GDzZAXF9u8JyNao1WqOPV2u0OHO1AAAwkMUxiQA0XetJrdHiIiuLE7VrDJ7sgFdNnSdB0AVQxvrrWhEq1Fp4ODuii28HUzePyCY1VespIasEVdVauDkxWZyovWLwZAccFHJ4OOsCqNYs0fJnnXwnuZzJr0RA07WexCG7XsHufL8QtVMMnuyEmPeUW2J88BTHZHGiRolDd+kFtcETk8WJiMGTnRCH7ozteRIEAX+m5AFg8ERUX2Mz7rgsCxFZRfC0ceNGhIeHQ6VSYciQIThx4kSLjvvss88gk8kwefJk/Ta1Wo3nn38e0dHRcHV1RVBQEGbOnIlr166ZqfXWwbuVS7SkF5Qjq6gSDnIZYkI8zdAyIttVf8ZdtUaLixlFANjzRNSeSR487dy5E4sWLcKKFSsQFxeHmJgYjBs3DtnZ2c0el5ycjMWLF2PEiBEG28vKyhAXF4dly5YhLi4Ou3btQnx8PCZOnGjOy5CcGDwZ2/Mk1nfqFeQOZyUXAyaqK7jeEi0J2SWorNaig5MDwn1cpWwaEUlI8uBp3bp1WLBgAebMmYOoqChs3rwZLi4u2LZtW5PHaDQaPPDAA1i1ahW6dOlicJ+Hhwd++eUXTJ06FT169MBNN92Ed999FydPnkRqaqq5L0cyrS2UWVsck+vZEdVXf9hOHLKLCmKyOFF7JmnwVFVVhZMnTyI2Nla/TS6XIzY2FkePHm3yuNWrV8PPzw/z5s1r0eMUFhZCJpPB09Oz0fsrKytRVFRkcLM14hItxgZPLI5J1LS6w3aCIOACk8WJCBIHTzk5OdBoNPD39zfY7u/vj8zMzEaPOXz4MLZu3YotW7a06DEqKirw/PPP4/7774e7e+Orn69ZswYeHh76W2hoqHEXYgVa0/NUUlmNS5m6QJHFMYkaCvLUlSooq9Igv0zNmXZEBMAKhu2MUVxcjBkzZmDLli3w9fW94f5qtRpTp06FIAjYtGlTk/stXboUhYWF+ltaWpopm20R+sWBjch5Op1aAK2gG5rwd1eZq2lENsvJQQF/dycAQHJuKf6qSRbnTDui9s1Bygf39fWFQqFAVlaWwfasrCwEBAQ02P/KlStITk7GhAkT9Nu0Wt1yJA4ODoiPj0dERASA2sApJSUF+/fvb7LXCQCcnJzg5ORkikuSjHcH43ueWKKA6MZCvFyQVVSJQ/HXUaHWwlWpQBdfJosTtWeS9jwplUoMGDAA+/bt02/TarXYt28fhg4d2mD/yMhInDt3DqdPn9bfJk6ciDFjxuD06dP64TYxcEpISMDevXvh4+NjsWuSSmtynsRk8YEMnoiaJCaN7z6vSyXoFeTBZHGidk7SnicAWLRoEWbNmoWBAwdi8ODBWL9+PUpLSzFnzhwAwMyZMxEcHIw1a9ZApVKhd+/eBseLSeDidrVajXvvvRdxcXH4/vvvodFo9PlT3t7eUCqVlrs4CxJznsqqNKhQa6BybL7sgEYr4FRqAQCgP4MnoiaJwVN8lm4x4F7BTfdiE1H7IHnwNG3aNFy/fh3Lly9HZmYm+vbti927d+uTyFNTUyGXt7yDLD09Hd9++y0AoG/fvgb3HThwAKNHjzZV062Ku8oBDnIZqrUC8suqEOjh3Oz+8ZnFKKmshqtSgcgA/jEgaoo4407EZHEikjx4AoCFCxdi4cKFjd538ODBZo/dsWOHwc/h4eEQBMFELbMdMpkMXq5KXC+uRF7pjYOnk6m6Ibt+nbyg4BAEUZPEnicRgycisqnZdtQ8/Yy7FizRcjKZyeJELVG358lFqUCXjh0kbA0RWQMGT3bEy1W3OHBuaeUN9xV7nljfiah5Yq0nAIgKdGdPLRExeLIn+vXtbjDjLruoAml55ZDLgL6hnhZoGZHtqlvrifWdiAhg8GRXvMRyBWXND9uJJQp6BLjDTeVo9nYR2bqwmkWA+4QweCIiK0kYJ9PwaWHP05/6xYA9zd0kIruwdHwk9l7Mwp19AqVuChFZAQZPdkS/vt0Nlmj5U18c09vsbSKyB/06eaFfJ+YHEpEOh+3siJjzlFfSdPBUodboV4bnTDsiIiLjMXiyI14tWBz4TFoBqrUC/NycGtSvISIiohtj8GRH9D1PzeQ81S1RIJNxyjUREZGxGDzZEX2pgrKqJqusn0zWBU/9mb9BRETUKgye7Ig4bKfWCCiprG5wvyAIdXqemCxORETUGgye7IizUgFnRwWAxofurlwvRUGZGk4OckQFcjFgIiKi1mDwZGeay3s6maJbzy4m1BNKB/7qiYiIWoN/Qe2MuL5dYzPuTurrOzHfiYiIqLUYPNkZb1fdGlx5pQ2XaKmtLM7giYiIqLUYPNkZb5eanqd6w3Z5pVX4+3opAM60IyIiagsGT3ZGXKIlt17wFFfT6xTR0VW/DxERERmPwZOd8XZpfHFgfYkCrmdHRETUJgye7ExTiwOLxTGZ70RERNQ2DJ7sjI9rw56nqmotzlwtAAAMCGfwRERE1BYMnuyMVyN1ni5cK0RltRZeLo7o4usqVdOIiIjsAoMnO+PdyLDdyTolCrgYMBERUdsweLIz4vp2heVqVGu0AOoGT0wWJyIiaisGT3bGq6bOkyDoAihBEFgck4iIyIQcpG4AmZaDQg4PZ0cUlquRX1aFsioNrhdXwlEhQ58QD6mbR0REZPMYPNkhb1clCsvVyC2pwrXCcgBAryAPqBwVEreMiIjI9nHYzg6JQ3f5ZVX4M5mLARMREZkSgyc7pJ9xV6rWJ4sPZH0nIiIik2DwZIfE4CklrxTxWcUAgP7seSIiIjIJBk92SCyUeeBSNgQB6OTtAj83lcStIiIisg8MnuyQuDjw5awSACxRQEREZEoMnuyQ2PMkYvBERERkOgye7JDY8yRi8ERERGQ6DJ7skHeH2uDJzckB3f3dJGwNERGRfWHwZIfq9jz1C/OCQs7FgImIiEyFwZMdqpvzNKATh+yIiIhMicGTHXJXOeh7m1gck4iIyLS4tp0dkslkmNIvGFeulzBZnIiIyMQYPNmpN++LkboJREREdonDdkRERERGYPBEREREZAQGT0RERERGYPBEREREZAQGT0RERERGsIrgaePGjQgPD4dKpcKQIUNw4sSJFh332WefQSaTYfLkyQbbBUHA8uXLERgYCGdnZ8TGxiIhIcEMLSciIqL2RvLgaefOnVi0aBFWrFiBuLg4xMTEYNy4ccjOzm72uOTkZCxevBgjRoxocN/atWvxzjvvYPPmzTh+/DhcXV0xbtw4VFRUmOsyiIiIqJ2QPHhat24dFixYgDlz5iAqKgqbN2+Gi4sLtm3b1uQxGo0GDzzwAFatWoUuXboY3CcIAtavX4+XXnoJkyZNQp8+ffDRRx/h2rVr+Prrr818NURERGTvJA2eqqqqcPLkScTGxuq3yeVyxMbG4ujRo00et3r1avj5+WHevHkN7ktKSkJmZqbBOT08PDBkyJBmz0lERETUEpJWGM/JyYFGo4G/v7/Bdn9/f1y6dKnRYw4fPoytW7fi9OnTjd6fmZmpP0f9c4r31VdZWYnKykr9z0VFRS29BCIiImpnJB+2M0ZxcTFmzJiBLVu2wNfX12TnXbNmDTw8PPS30NBQk52biIiI7IukPU++vr5QKBTIysoy2J6VlYWAgIAG+1+5cgXJycmYMGGCfptWqwUAODg4ID4+Xn9cVlYWAgMDDc7Zt2/fRtuxdOlSLFq0SP9zUVERAygiIiJqlKQ9T0qlEgMGDMC+ffv027RaLfbt24ehQ4c22D8yMhLnzp3D6dOn9beJEydizJgxOH36NEJDQ9G5c2cEBAQYnLOoqAjHjx9v9JwA4OTkBHd3d4MbERERUWMk7XkCgEWLFmHWrFkYOHAgBg8ejPXr16O0tBRz5swBAMycORPBwcFYs2YNVCoVevfubXC8p6cnABhsf/rpp/HKK6+gW7du6Ny5M5YtW4agoKAG9aCIiIiIjCV58DRt2jRcv34dy5cvR2ZmJvr27Yvdu3frE75TU1MhlxvXQfbcc8+htLQUDz30EAoKCnDzzTdj9+7dUKlULTpeEAQATBwnIiKyJeLfbfHvuLnIBHM/gg26evUqc56IiIhsVFpaGkJCQsx2fgZPjdBqtbh27Rrc3Nwgk8mkbo4+gT0tLa1d52PxedDh81CLz4UOnwcdPg+12utzIQgCiouLERQUZPSolTEkH7azRnK53KwRa2sxmV2Hz4MOn4dafC50+Dzo8Hmo1R6fCw8PD7M/hk3VeSIiIiKSGoMnIiIiIiMweLIBTk5OWLFiBZycnKRuiqT4POjweajF50KHz4MOn4dafC7MiwnjREREREZgzxMRERGRERg8ERERERmBwRMRERGRERg8ERERERmBwZPE1qxZg0GDBsHNzQ1+fn6YPHky4uPjmz1mx44dkMlkBreWrttnrVauXNngmiIjI5s95vPPP0dkZCRUKhWio6Px448/Wqi15hUeHt7guZDJZHj88ccb3d9eXg+//vorJkyYgKCgIMhkMnz99dcG9wuCgOXLlyMwMBDOzs6IjY1FQkLCDc+7ceNGhIeHQ6VSYciQIThx4oSZrsA0mnse1Go1nn/+eURHR8PV1RVBQUGYOXMmrl271uw5W/P+sgY3ek3Mnj27wXXdfvvtNzyvPb0mADT6eSGTyfDmm282eU5bfU1YCwZPEjt06BAef/xxHDt2DL/88gvUajVuu+02lJaWNnucu7s7MjIy9LeUlBQLtdh8evXqZXBNhw8fbnLf33//Hffffz/mzZuHU6dOYfLkyZg8eTLOnz9vwRabxx9//GHwPPzyyy8AgPvuu6/JY+zh9VBaWoqYmBhs3Lix0fvXrl2Ld955B5s3b8bx48fh6uqKcePGoaKioslz7ty5E4sWLcKKFSsQFxeHmJgYjBs3DtnZ2ea6jDZr7nkoKytDXFwcli1bhri4OOzatQvx8fGYOHHiDc9rzPvLWtzoNQEAt99+u8F1/e9//2v2nPb2mgBgcP0ZGRnYtm0bZDIZ7rnnnmbPa4uvCashkFXJzs4WAAiHDh1qcp/t27cLHh4elmuUBaxYsUKIiYlp8f5Tp04V7rzzToNtQ4YMER5++GETt0x6Tz31lBARESFotdpG77fH1wMA4auvvtL/rNVqhYCAAOHNN9/UbysoKBCcnJyE//3vf02eZ/DgwcLjjz+u/1mj0QhBQUHCmjVrzNJuU6v/PDTmxIkTAgAhJSWlyX2MfX9Zo8aei1mzZgmTJk0y6jzt4TUxadIk4ZZbbml2H3t4TUiJPU9WprCwEADg7e3d7H4lJSUICwtDaGgoJk2ahAsXLliieWaVkJCAoKAgdOnSBQ888ABSU1Ob3Pfo0aOIjY012DZu3DgcPXrU3M20qKqqKnzyySeYO3dus4tU2+Proa6kpCRkZmYa/M49PDwwZMiQJn/nVVVVOHnypMExcrkcsbGxdvU6KSwshEwmg6enZ7P7GfP+siUHDx6En58fevTogUcffRS5ublN7tseXhNZWVn44YcfMG/evBvua6+vCUtg8GRFtFotnn76aQwfPhy9e/ducr8ePXpg27Zt+Oabb/DJJ59Aq9Vi2LBhuHr1qgVba1pDhgzBjh07sHv3bmzatAlJSUkYMWIEiouLG90/MzMT/v7+Btv8/f2RmZlpieZazNdff42CggLMnj27yX3s8fVQn/h7NeZ3npOTA41GY9evk4qKCjz//PO4//77m1381dj3l624/fbb8dFHH2Hfvn144403cOjQIYwfPx4ajabR/dvDa+LDDz+Em5sbpkyZ0ux+9vqasBQHqRtAtR5//HGcP3/+huPOQ4cOxdChQ/U/Dxs2DD179sT777+Pl19+2dzNNIvx48fr/9+nTx8MGTIEYWFh+L//+78WfYOyV1u3bsX48eMRFBTU5D72+HqgG1Or1Zg6dSoEQcCmTZua3dde31/Tp0/X/z86Ohp9+vRBREQEDh48iLFjx0rYMuls27YNDzzwwA0njdjra8JS2PNkJRYuXIjvv/8eBw4cQEhIiFHHOjo6ol+/fkhMTDRT6yzP09MT3bt3b/KaAgICkJWVZbAtKysLAQEBlmieRaSkpGDv3r2YP3++UcfZ4+tB/L0a8zv39fWFQqGwy9eJGDilpKTgl19+abbXqTE3en/Zqi5dusDX17fJ67Ln1wQA/Pbbb4iPjzf6MwOw39eEuTB4kpggCFi4cCG++uor7N+/H507dzb6HBqNBufOnUNgYKAZWiiNkpISXLlypclrGjp0KPbt22ew7ZdffjHogbF127dvh5+fH+68806jjrPH10Pnzp0REBBg8DsvKirC8ePHm/ydK5VKDBgwwOAYrVaLffv22fTrRAycEhISsHfvXvj4+Bh9jhu9v2zV1atXkZub2+R12etrQrR161YMGDAAMTExRh9rr68Js5E6Y729e/TRRwUPDw/h4MGDQkZGhv5WVlam32fGjBnCCy+8oP951apVwp49e4QrV64IJ0+eFKZPny6oVCrhwoULUlyCSTz77LPCwYMHhaSkJOHIkSNCbGys4OvrK2RnZwuC0PA5OHLkiODg4CC89dZbwsWLF4UVK1YIjo6Owrlz56S6BJPSaDRCp06dhOeff77Bffb6eiguLhZOnTolnDp1SgAgrFu3Tjh16pR+Ftnrr78ueHp6Ct98841w9uxZYdKkSULnzp2F8vJy/TluueUWYcOGDfqfP/vsM8HJyUnYsWOH8NdffwkPPfSQ4OnpKWRmZlr8+lqqueehqqpKmDhxohASEiKcPn3a4DOjsrJSf476z8ON3l/Wqrnnori4WFi8eLFw9OhRISkpSdi7d6/Qv39/oVu3bkJFRYX+HPb+mhAVFhYKLi4uwqZNmxo9h728JqwFgyeJAWj0tn37dv0+o0aNEmbNmqX/+emnnxY6deokKJVKwd/fX7jjjjuEuLg4yzfehKZNmyYEBgYKSqVSCA4OFqZNmyYkJibq76//HAiCIPzf//2f0L17d0GpVAq9evUSfvjhBwu32nz27NkjABDi4+Mb3Gevr4cDBw40+l4Qr1Wr1QrLli0T/P39BScnJ2Hs2LENnp+wsDBhxYoVBts2bNigf34GDx4sHDt2zEJX1DrNPQ9JSUlNfmYcOHBAf476z8ON3l/WqrnnoqysTLjtttuEjh07Co6OjkJYWJiwYMGCBkGQvb8mRO+//77g7OwsFBQUNHoOe3lNWAuZIAiCWbu2iIiIiOwIc56IiIiIjMDgiYiIiMgIDJ6IiIiIjMDgiYiIiMgIDJ6IiIiIjMDgiYiIiMgIDJ6IiIiIjMDgiYjIBA4ePAiZTIaCggKpm0JEZsbgiYjMYvbs2ZDJZHj99dcNtn/99deQyWQWb49MJmv2tnLlyjadf9iwYcjIyICHh4dpGkxEVstB6gYQkf1SqVR444038PDDD8PLy0vStmRkZOj/v3PnTixfvhzx8fH6bR06dGjT+ZVKJQICAtp0DiKyDex5IiKziY2NRUBAANasWdPkPitXrkTfvn0Ntq1fvx7h4eH6n2fPno3Jkyfjtddeg7+/Pzw9PbF69WpUV1djyZIl8Pb2RkhICLZv397k4wQEBOhvHh4ekMlk+p/9/Pywbt06hISEwMnJCX379sXu3bv1xyYnJ0Mmk+Gzzz7DsGHDoFKp0Lt3bxw6dEi/T2PDdkeOHMHo0aPh4uICLy8vjBs3Dvn5+QCAL774AtHR0XB2doaPjw9iY2NRWlrawmeWiKTE4ImIzEahUOC1117Dhg0bcPXq1Tada//+/bh27Rp+/fVXrFu3DitWrMBdd90FLy8vHD9+HI888ggefvjhVj3O22+/jX/961946623cPbsWYwbNw4TJ05EQkKCwX5LlizBs88+i1OnTmHo0KGYMGECcnNzGz3n6dOnMXbsWERFReHo0aM4fPgwJkyYAI1Gg4yMDNx///2YO3cuLl68iIMHD2LKlCngUqNEtoHBExGZ1d13342+fftixYoVbTqPt7c33nnnHfTo0QNz585Fjx49UFZWhn/+85/o1q0bli5dCqVSicOHDxt97rfeegvPP/88pk+fjh49euCNN95A3759sX79eoP9Fi5ciHvuuQc9e/bEpk2b4OHhga1btzZ6zrVr12LgwIF47733EBMTg169emHhwoXw9fVFRkYGqqurMWXKFISHhyM6OhqPPfZYm4cOicgyGDwRkdm98cYb+PDDD3Hx4sVWn6NXr16Qy2s/svz9/REdHa3/WaFQwMfHB9nZ2Uadt6ioCNeuXcPw4cMNtg8fPrxBe4cOHar/v4ODAwYOHNjkNYk9T42JiYnB2LFjER0djfvuuw9btmzRD+cRkfVj8EREZjdy5EiMGzcOS5cubXCfXC5vMFylVqsb7Ofo6Gjws0wma3SbVqs1QYvbztnZucn7FAoFfvnlF/z000+IiorChg0b0KNHDyQlJVmwhUTUWgyeiMgiXn/9dXz33Xc4evSowfaOHTsiMzPTIIA6ffq0xdrl7u6OoKAgHDlyxGD7kSNHEBUVZbDt2LFj+v9XV1fj5MmT6NmzZ6Pn7dOnD/bt29fk48pkMgwfPhyrVq3CqVOnoFQq8dVXX7XhSojIUliqgIgsIjo6Gg888ADeeecdg+2jR4/G9evXsXbtWtx7773YvXs3fvrpJ7i7u1usbUuWLMGKFSsQERGBvn37Yvv27Th9+jQ+/fRTg/02btyIbt26oWfPnvj3v/+N/Px8zJ07t9FzLl26VJ/L9Mgjj0CpVOLAgQO47777cOXKFezbtw+33XYb/Pz8cPz4cVy/fr3JQIyIrAt7nojIYlavXt1gWK1nz5547733sHHjRsTExODEiRNYvHixRdv15JNPYtGiRXj22WcRHR2N3bt349tvv0W3bt0M9nv99dfx+uuvIyYmBocPH8a3334LX1/fRs/ZvXt3/Pzzzzhz5gwGDx6MoUOH4ptvvoGDgwPc3d3x66+/4o477kD37t3x0ksv4V//+hfGjx9vicslojaSCZwbS0TUrOTkZHTu3BmnTp1qUJOKiNof9jwRERERGYHBExEREZEROGxHREREZAT2PBEREREZgcETERERkREYPBEREREZgcETERERkREYPBEREREZgcETERERkREYPBEREREZgcETERERkREYPBEREREZ4f8B+8UE2QYQ8DkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "import matplotlib.pyplot as plt\n",
    "import gensim.corpora as corpora\n",
    "from gensim.models import TfidfModel\n",
    "from gensim.models import CoherenceModel\n",
    "from gensim.models.ldamodel import LdaModel\n",
    "import pyLDAvis\n",
    "import pyLDAvis.gensim_models as gensimvis\n",
    "from tqdm import tqdm\n",
    "\n",
    "# Download stopwords from NLTK if you haven't already\n",
    "# nltk.download('stopwords')\n",
    "# nltk.download('punkt')\n",
    "\n",
    "\n",
    "# Preprocessing the text in the 'Abstract' column\n",
    "def preprocess_text(text):\n",
    "    stop_words = set(stopwords.words('english'))\n",
    "    tokens = word_tokenize(text.lower())\n",
    "    tokens = [word for word in tokens if word.isalpha()]  # Keeping only alphabetical strings\n",
    "    tokens = [word for word in tokens if len(word) >= 3]  # Keeping words with at least three characters\n",
    "    tokens = [word for word in tokens if word not in stop_words]  # Removing stopwords\n",
    "    return tokens\n",
    "\n",
    "df = filtered_papers\n",
    "\n",
    "# Applying preprocessing to each abstract in the DataFrame\n",
    "df['tokens'] = df['Abstract'].apply(preprocess_text)\n",
    "\n",
    "# Creating a dictionary and corpus for LDA\n",
    "dictionary = corpora.Dictionary(df['tokens'])\n",
    "bow_corpus = [dictionary.doc2bow(text) for text in df['tokens']]\n",
    "\n",
    "# Creating a TF-IDF model and transforming the corpus\n",
    "tfidf_model = TfidfModel(bow_corpus)\n",
    "tfidf_corpus = tfidf_model[bow_corpus]\n",
    "\n",
    "def compute_coherence_values(dictionary, corpus, texts, limit, start=2, step=3):\n",
    "    coherence_values = []\n",
    "    model_list = []\n",
    "    for num_topics in tqdm(range(start, limit, step)):\n",
    "        model = LdaModel(corpus=corpus, id2word=dictionary, num_topics=num_topics)\n",
    "        model_list.append(model)\n",
    "        coherencemodel = CoherenceModel(model=model, texts=texts, dictionary=dictionary, coherence='c_v')\n",
    "        coherence_values.append(coherencemodel.get_coherence())\n",
    "    return model_list, coherence_values\n",
    "\n",
    "# Calculating coherence values for different numbers of topics\n",
    "model_list, coherence_values = compute_coherence_values(dictionary=dictionary, corpus=tfidf_corpus, texts=df['tokens'], start=2, limit=20, step=1)\n",
    "\n",
    "# Plotting coherence values\n",
    "x = range(2, 20, 1)\n",
    "plt.plot(x, coherence_values)\n",
    "plt.xlabel(\"Num Topics\")\n",
    "plt.ylabel(\"Coherence score\")\n",
    "plt.legend((\"coherence_values\"), loc='best')\n",
    "plt.title('Coherence Scores for Different Numbers of Topics, using TF-IDF')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal Model:\n",
      "Topic 1: abstract, available, fuzzy, model, cognitive\n",
      "Topic 2: cognitive, maps, fcm, fuzzy, causal\n",
      "\n",
      "\n",
      "\n",
      "Elbow Rule Model:\n",
      "Topic 1: marketing, drought, knowledge, political, tmt\n",
      "Topic 2: abstract, available, fcm, fuzzy, fcms\n",
      "Topic 3: fuzzy, maps, cognitive, fcm, model\n",
      "Topic 4: management, problem, knowledge, strategic, mental\n",
      "Topic 5: causal, maps, teams, systems, agents\n",
      "Topic 6: risks, privacy, administrative, erp, semantic\n",
      "Topic 7: modeling, cognitive, environmental, turnover, planning\n",
      "Topic 8: available, abstract, cognitive, knowledge, managers\n",
      "Topic 9: fcm, cognitive, fuzzy, maps, map\n",
      "Topic 10: abstract, available, organisation, organizational, ontology\n"
     ]
    }
   ],
   "source": [
    "def print_topics(lda_model, num_words=5):\n",
    "    for index, topic in lda_model.print_topics(num_words=num_words):\n",
    "        terms = []\n",
    "        term_weight_pairs = topic.split(' + ')\n",
    "        for pair in term_weight_pairs:\n",
    "            weight, term = pair.split('*')\n",
    "            terms.append(term.strip('\"'))\n",
    "        print(f\"Topic {index+1}: {', '.join(terms)}\")\n",
    "\n",
    "\n",
    "# Select the model with the highest coherence and visualize the topics\n",
    "optimal_model = model_list[coherence_values.index(max(coherence_values))]\n",
    "\n",
    "# elbow rule, we want model with highest coherence score before flattening out\n",
    "elbow_rule_model = model_list[8]\n",
    "\n",
    "\n",
    "# Example usage:\n",
    "print(\"Optimal Model:\")\n",
    "print_topics(optimal_model)\n",
    "print('\\n\\n')\n",
    "\n",
    "\n",
    "print(\"Elbow Rule Model:\")\n",
    "print_topics(elbow_rule_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vis = gensimvis.prepare(elbow_rule_model, tfidf_corpus, dictionary)\n",
    "pyLDAvis.display(vis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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