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#$git clone https://huggingface.co/spaces/aboltachka/rr_detector
#####################
# IMPORT PACKAGES
#####################
import gradio as gr
import pandas as pd
import re
import os
dirname = os.path.dirname(__file__)
#####################
# ALGORITHM SETUP
#####################
group_control = [('control',0,re.compile(r'\bminimum wage\b')),
('control',1,re.compile(r'\bcrime[a-zA-Z]{0,7}\b')),
('control',2,re.compile(r'\bimmigra[a-zA-Z]{0,4}\b')),
('control',3,re.compile(r'\bmigra[a-zA-Z]{0,4}\b')),
('control',4,re.compile(r'\bsingle mother[a-zA-Z]{0,1}\b')),
('control',5,re.compile(r'\blone mother[a-zA-Z]{0,1}\b')),
('control',6,re.compile(r'\bone parent[a-zA-Z]{0,3}\b')),
('control',7,re.compile(r'\blone parent[a-zA-Z]{0,3}\b')),
('control',8,re.compile(r'\bsingle parent[a-zA-Z]{0,3}\b')), #change from {0,1} to {0,3} for parenting
('control',9,re.compile(r'\bsingle headed\b')),
('control',10,re.compile(r'\blone headed\b')),
('control',11,re.compile(r'\bsingle headed\b')),
('control',12,re.compile(r'\blone headed\b')),
('control',13,re.compile(r'\bdoctor\b')),
('control',14,re.compile(r'\bphysician\b')),
('control',15,re.compile(r'\bself-employed\b')),
('control',16,re.compile(r'\bentrepreneur\b'))]
group_issue = [('issue',0,re.compile(r'\bdiscriminat[a-zA-Z]{0,5}\b')), # changed from {0,4} to {0,5}, e/g Discriminatively
('issue',1,re.compile(r'\bprejudi[a-zA-Z]{0,4}\b')), # changed from {0,3} to {0,4}, e/g Prejudicing
('issue',2,re.compile(r'\brac[a-zA-Z]{0,3} bias[a-zA-Z]{0,3}\b')),
('issue',3,re.compile(r'\brac[a-zA-Z]{0,3} disparit[a-zA-Z]{0,3}\b')),
('issue',4,re.compile(r'\brac[a-zA-Z]{0,3} stereotyp[a-zA-Z]{0,3}\b')),
('issue',5,re.compile(r'\breconstruction[a-zA-Z]{0,1}\b')),
('issue',6,re.compile(r'\bdesegregat[a-zA-Z]{0,3}\b')),
('issue',7,re.compile(r'\bjim crow\b')),
('issue',8,re.compile(r'\blynch[a-zA-Z]{0,5}\b')), # changed from {0,3} to {0,5}, e/g lynchings
('issue',9,re.compile(r'\bwhitecapping\b')),
('issue',10,re.compile(r'\bcivil rights\b')),
('issue',11,re.compile(r'\brace riot[a-zA-Z]{0,3}\b')),
('issue',12,re.compile(r'\bemancipat[a-zA-Z]{0,3}\b')),
('issue',13,re.compile(r'\bslave[a-zA-Z]{0,2}\b')),
('issue',14,re.compile(r'\brace relation[a-zA-Z]{0,1}\b')),
('issue',15,re.compile(r'\bstatistical discrimination[a-zA-Z]{0,1}\b')),
('issue',16,re.compile(r'\banimus\b')),
('issue',17,re.compile(r'\banimosit[a-zA-Z]{0,3}\b')),
('issue',18,re.compile(r'\bsegregat[a-zA-Z]{0,3}\b')),
('issue',19,re.compile(r'\brac[a-zA-Z]{0,3} identit[a-zA-Z]{0,3}\b')),
('issue',21,re.compile(r'\beugenics\b')),
('issue',22,re.compile(r'\brac[a-zA-Z]{0,3} profiling\b')),
('issue',23,re.compile(r'\baffirmative action[a-zA-Z]{0,1}\b')),
('issue',24,re.compile(r'\btipping point\b')),
('issue',25,re.compile(r'\bblack-white\b')),
('issue',26,re.compile(r'\brac[a-zA-Z]{0,3} gap[a-zA-Z]{0,1}\b')),
('issue',27,re.compile(r'\brac[a-zA-Z]{0,3} differen[a-zA-Z]{0,4}\b')),
('issue',28,re.compile(r'\brac[a-zA-Z]{0,3} composition[a-zA-Z]{0,1}\b')),
('issue',29,re.compile(r'\brac[a-zA-Z]{0,3} integration[a-zA-Z]{0,1}\b')),
('issue',30,re.compile(r'\brac[a-zA-Z]{0,3} interact[a-zA-Z]{0,4}\b')),
('issue',31,re.compile(r'\brac[a-zA-Z]{0,3} inequalit[a-zA-Z]{0,3}\b')),
('issue',33,re.compile(r'\bnegro-white\b')),
('issue',34,re.compile(r'\banti-discrimination\b')),
('issue',35,re.compile(r'\bantidiscrimination\b')),
('issue',37,re.compile(r'\bethnic bias[a-zA-Z]{0,3}\b')),
('issue',38,re.compile(r'\bethnic disparit[a-zA-Z]{0,3}\b')),
('issue',39,re.compile(r'\bethnic stereotyp[a-zA-Z]{0,3}\b')),
('issue',40,re.compile(r'\bethnic gap[a-zA-Z]{0,1}\b')),
('issue',41,re.compile(r'\bethnic differen[a-zA-Z]{0,4}\b')),
('issue',42,re.compile(r'\bethnic composition[a-zA-Z]{0,1}\b')),
('issue',43,re.compile(r'\bethnic integration[a-zA-Z]{0,1}\b')),
('issue',44,re.compile(r'\bethnic interact[a-zA-Z]{0,4}\b')),
('issue',45,re.compile(r'\bethnic inequalit[a-zA-Z]{0,3}\b')),
('issue',46,re.compile(r'\bpostbellum\b')),
('issue',47,re.compile(r'\bdisadvantage\b')),
('issue',48,re.compile(r'\bdisadvantaged\b')),
('issue',49,re.compile(r'\battitude[a-zA-Z]{0,1}\b')),
('issue',50,re.compile(r'\bgap[a-zA-Z]{0,1}\b')),
('issue',51,re.compile(r'\bapartheid\b')),
('issue',52,re.compile(r'\brepresentation\b')),
('issue',53,re.compile(r'\bantisemitism\b')),
('issue',54,re.compile(r'\banti-semitic\b')),
('issue',55,re.compile(r'\banti-black\b')),
('issue',56,re.compile(r'\bidentity\b')),
('issue',57,re.compile(r'\bidentities\b')),
('issue',58,re.compile(r'\bsouthern farm\b')),
('issue',59,re.compile(r'\bracial heterogene[a-zA-Z]{0,5}\b')),
('issue',60,re.compile(r'\bethnic heterogene[a-zA-Z]{0,5}\b')),
('issue',61,re.compile(r'\bacting white\b')),
('issue',62,re.compile(r'\baffirmative-action[a-zA-Z]{0,1}\b')),
('issue',63,re.compile(r'\bhatred\b')),
('issue',64,re.compile(r'\bsocial activis[a-zA-Z]{0,1}\b')),
('issue',65,re.compile(r'\bsocial-activis[a-zA-Z]{0,1}\b')),
('issue',66,re.compile(r'\bethnic fragmentation[a-zA-Z]{0,1}\b')),
('issue',67,re.compile(r'\bethnic-fragmentation[a-zA-Z]{0,1}\b')),
('issue',68,re.compile(r'\bsocial fragmentation[a-zA-Z]{0,1}\b')),
('issue',69,re.compile(r'\bsocial-fragmentation[a-zA-Z]{0,1}\b')),
('issue',70,re.compile(r'\bracial fragmentation[a-zA-Z]{0,1}\b')),
('issue',71,re.compile(r'\bracial-fragmentation[a-zA-Z]{0,1}\b')),
('issue',72,re.compile(r'\bethnic division[a-zA-Z]{0,1}\b')),
('issue',73,re.compile(r'\bethnic-division[a-zA-Z]{0,1}\b')),
('issue',74,re.compile(r'\bsocial division[a-zA-Z]{0,1}\b')),
('issue',75,re.compile(r'\bsocial-division[a-zA-Z]{0,1}\b')),
('issue',76,re.compile(r'\bracial division[a-zA-Z]{0,1}\b')),
('issue',77,re.compile(r'\bracial-division[a-zA-Z]{0,1}\b')),
('issue',78,re.compile(r'\bethnic exclusion[a-zA-Z]{0,1}\b')),
('issue',79,re.compile(r'\bethnic-exclusion[a-zA-Z]{0,1}\b')),
('issue',80,re.compile(r'\bsocial exclusion[a-zA-Z]{0,1}\b')),
('issue',81,re.compile(r'\bsocial-exclusion[a-zA-Z]{0,1}\b')),
('issue',82,re.compile(r'\bracial exclusion[a-zA-Z]{0,1}\b')),
('issue',83,re.compile(r'\bracial-exclusion[a-zA-Z]{0,1}\b')),
('issue',84,re.compile(r'\bethnic diversity\b')),
('issue',85,re.compile(r'\bethnic-diversity\b')),
('issue',86,re.compile(r'\bsocial diversity\b')),
('issue',87,re.compile(r'\bsocial-diversity\b')),
('issue',88,re.compile(r'\bracial diversity\b')),
('issue',89,re.compile(r'\bracial-diversity\b')),
('issue',90,re.compile(r'\bthe great migration\b')),
('issue',91,re.compile(r'\bblack youth[a-zA-Z]{0,1}\b')),
('issue',92,re.compile(r'\bblack‐white\b')),
('issue',93,re.compile(r'\b-group bias\b')),
('issue',94,re.compile(r'\btuskegee\b')),
('issue',95,re.compile(r'\bingroup\b')),
('issue',96,re.compile(r'\bin-group\b')),
('issue',97,re.compile(r'\boutgroup\b')),
('issue',98,re.compile(r'\bout-group\b')),
('issue',99,re.compile(r'\binter-group\b')),
('issue',100,re.compile(r'\bintergroup\b')),
('issue',101,re.compile(r'\binequality\b')),
('issue',102,re.compile(r'\bstratification\b')),
('issue',103,re.compile(r'\bimplicit bias[a-zA-Z]{0,2}\b')),
('issue',104,re.compile(r'\bblack vot[a-zA-Z]{0,3}\b')),
('issue',105,re.compile(r'\bpolitical disenfranchisement\b')),
('issue',106,re.compile(r'\binstitutional discrimination\b')),
('issue',107,re.compile(r'\bstructural discrimination\b')),
('issue',108,re.compile(r'\binstitutional racism\b')),
('issue',109,re.compile(r'\bsystemic racism\b')),
('issue',110,re.compile(r'\bdifferential\b')),
('issue',111,re.compile(r'\b-differential\b')),
('issue',112,re.compile(r'\bunderrepresent[a-zA-Z]{0,3}\b')),
('issue',113,re.compile(r'\bexploitation\b')),
('issue',114,re.compile(r'\boppress[a-zA-Z]{0,3}\b')),
('issue',115, re.compile(r'\bschool[a-zA-Z]{0,3}\b')),
('issue',116, re.compile(r'\beducat[a-zA-Z]{0,5}\b')),
('issue',117, re.compile(r'\bdevelopment\b')),
('issue',118, re.compile(r'\bpoverty\b')),
('issue',119, re.compile(r'\bliving standard\b')),
('issue',120, re.compile(r'\bwelfare\b'))]
group_ethnicity = [('ethnicity',0,re.compile(r'\brac[a-zA-Z]{0,3}\b')),
('ethnicity',1,re.compile(r'\bafrican-american[a-zA-Z]{0,1}\b')),
('ethnicity',2,re.compile(r'\bafrican american[a-zA-Z]{0,1}\b')),
('ethnicity',3,re.compile(r'\bperson[a-zA-Z]{0,1} of color[a-zA-Z]{0,1}\b')),
('ethnicity',4,re.compile(r'\bethnic[a-zA-Z]{0,4}\b')),
('ethnicity',5,re.compile(r'\bhispanic[a-zA-Z]{0,1}\b')),
('ethnicity',6,re.compile(r'\blatino[a-zA-Z]{0,1}\b')),
('ethnicity',7,re.compile(r'\bblack[a-zA-Z]{0,1}\b')),
#('ethnicity',8,re.compile(r'\bwhite[s]{0,1}\b')),
('ethnicity',11,re.compile(r'\bnegro[a-zA-Z]{0,2}\b')),
('ethnicity',15,re.compile(r'\bsouth asian[a-zA-Z]{0,1}\b')),
('ethnicity',17,re.compile(r'\bunderrepresented minorit[a-zA-Z]{0,3}\b')),
('ethnicity',18,re.compile(r'\bethnic minorit[a-zA-Z]{0,3}\b')),
('ethnicity',21,re.compile(r'\bblack-american[a-zA-Z]{0,1}\b')),
('ethnicity',22,re.compile(r'\bindian-american[a-zA-Z]{0,1}\b')),
('ethnicity',23,re.compile(r'\bjapanese-american[a-zA-Z]{0,1}\b')),
('ethnicity',24,re.compile(r'\bchinese-american[a-zA-Z]{0,1}\b')),
('ethnicity',25,re.compile(r'\bmexican-american[a-zA-Z]{0,1}\b')),
('ethnicity',26,re.compile(r'\bhispanic-american[a-zA-Z]{0,1}\b')),
('ethnicity',27,re.compile(r'\blatino-american[a-zA-Z]{0,1}\b')),
('ethnicity',28,re.compile(r'\bnative-american[a-zA-Z]{0,1}\b')),
('ethnicity',29,re.compile(r'\bamerican-indian[a-zA-Z]{0,1}\b')),
('ethnicity',30,re.compile(r'\borientals\b')),
('ethnicity',32,re.compile(r'\bkorean-american[a-zA-Z]{0,1}\b')),
('ethnicity',34,re.compile(r'\bvietnamese-american[a-zA-Z]{0,1}\b')),
('ethnicity',35,re.compile(r'\bnon-white[a-zA-Z]{0,1}\b')),
('ethnicity',36,re.compile(r'\bcolored[a-zA-Z]{0,1}\b')),
('ethnicity',37,re.compile(r'\bcaste[a-zA-Z]{0,1}\b')),
('ethnicity',38,re.compile(r'\bdisadvantaged minor[a-zA-Z]{0,5}\b')),
('ethnicity',39,re.compile(r'\badvantaged-group[a-zA-Z]{0,1}\b')),
('ethnicity',40,re.compile(r'\badvantaged group[a-zA-Z]{0,1}\b')),
('ethnicity',41,re.compile(r'\bdominant group[a-zA-Z]{0,1}\b')),
('ethnicity',42,re.compile(r'\bdominant-group[a-zA-Z]{0,1}\b')),
('ethnicity',43,re.compile(r'\bnon-western[a-zA-Z]{0,1}\b')),
('ethnicity',44,re.compile(r'\bblack american[a-zA-Z]{0,1}\b')),
('ethnicity',45,re.compile(r'\bindian american[a-zA-Z]{0,1}\b')),
('ethnicity',46,re.compile(r'\bjapanese american[a-zA-Z]{0,1}\b')),
('ethnicity',47,re.compile(r'\bchinese american[a-zA-Z]{0,1}\b')),
('ethnicity',48,re.compile(r'\bmexican american[a-zA-Z]{0,1}\b')),
('ethnicity',49,re.compile(r'\bhispanic american[a-zA-Z]{0,1}\b')),
('ethnicity',50,re.compile(r'\blatino american[a-zA-Z]{0,1}\b')),
('ethnicity',51,re.compile(r'\bnative american[a-zA-Z]{0,1}\b')),
('ethnicity',52,re.compile(r'\bamerican indian[a-zA-Z]{0,1}\b')),
('ethnicity',55,re.compile(r'\bnative[a-zA-Z]{0,1}\b')),
('ethnicity',56,re.compile(r'\bcaucasian[a-zA-Z]{0,1}\b')),
('ethnicity',57,re.compile(r'\bjew[a-zA-Z]{0,3}\b')),
('ethnicity',58,re.compile(r'\bhebrew[a-zA-Z]{0,1}\b')),
('ethnicity',59,re.compile(r'\byiddish\b')),
('ethnicity',60,re.compile(r'\bmuslim[a-zA-Z]{0,1}\b')),
('ethnicity',61,re.compile(r'\bislam[a-zA-Z]{0,2}\b')),
('ethnicity',62,re.compile(r'\bgay\b')),
('ethnicity',63,re.compile(r'\bqueer\b')),
('ethnicity',64,re.compile(r'\bhomosexual\b')),
('ethnicity',65,re.compile(r'\bhomo-sexual\b')),
('ethnicity',66,re.compile(r'\blesbian\b')),
('ethnicity',67,re.compile(r'\bpeople of colo[a-zA-Z]{0,1}r\b')),
('ethnicity',68,re.compile(r'\bpeople-of-colo[a-zA-Z]{0,1}r\b')),
('ethnicity',69,re.compile(r'\barab\b')),
# aboriginal indigenous
('ethnicity',70,re.compile(r'\baboriginal\b')),
('ethnicity',71,re.compile(r'\bindigenous\b')),
# tribes
('ethnicity',72,re.compile(r'\bnavajo[a-zA-Z]{0,3}\b')),
('ethnicity',73,re.compile(r'\bcherokee[a-zA-Z]{0,3}\b')),
('ethnicity',74,re.compile(r'\bcherokeean[a-zA-Z]{0,3}\b')),
('ethnicity',75,re.compile(r'\bsioux\b')),
('ethnicity',76,re.compile(r'\bsiouan\b')),
('ethnicity',77,re.compile(r'\bchippewa[a-zA-Z]{0,3}\b')),
('ethnicity',78,re.compile(r'\bchoctaw[a-zA-Z]{0,3}\b'))]
group_blackball = [('blackball',0, re.compile(r'\bblack.{0,3}market[a-zA-Z- ]{0,3}\b')),
('blackball',1, re.compile(r'\bblack.{0,3}economy\b')),
('blackball',2, re.compile(r'\bblack.{0,3}box.{0,3}\b')),
('blackball',3, re.compile(r'\bblack.{0,3}card[a-zA-Z]{0,1}\b')),
('blackball',4, re.compile(r'\bblack.{0,3}scholes\b')),
('blackball',5, re.compile(r'\barms.{0,3}rac.{0,3}\b')),
('blackball',6, re.compile(r'\bpatent.{0,3}rac.{0,3}\b')),
('blackball',7, re.compile(r'\brat.{0,3}.{0,3}rac.{0,3}\b')),
('blackball',8, re.compile(r'\bpriority.{0,3}rac.{0,3}\b')),
('blackball',9, re.compile(r'\belectoral.{0,3}rac.{0,3}\b')),
('blackball',10, re.compile(r'\brd.{0,3}rac.{0,3}\b')),
('blackball',11, re.compile(r'\bwhite.{0,3}collar\b')),
('blackball',12, re.compile(r'\bwhite.{0,3}noise\b')),
('blackball',13, re.compile(r'\brace[s]{0,1} between\b')),
('blackball',14, re.compile(r'\brac.*prize.{0,3}\b')),
('blackball',15, re.compile(r'\bprize.*rac.{0,3}\b')),
('blackball',16, re.compile(r'\brac.*winner{0,3}\b')),
('blackball',17, re.compile(r'\bwinner.*rac.{0,3}\b')),
('blackball',18, re.compile(r'\bhorse.*rac.{0,3}\b')),
('blackball',19, re.compile(r'\brac.*horse.{0,3}\b')),
('blackball',20, re.compile(r'\brival\b')),
('blackball',21, re.compile(r'\br d.{0,3}rac.{0,3}\b')),
('blackball',22, re.compile(r'\bblack swan\b'))]
search_regexs_groups = [group_control,group_issue,group_ethnicity,group_blackball]
group_main_american = [1,2,3,5,6,7,11,21,25,26,27,28,29,36,44,48,49,50,51,52,67,68,]
group_minor = [15,22,23,24,30,32,34,45,46,47,55,56,]
group_religious = [57,58,59,60,61,69,]
group_sexual = [62,63,64,65,66]
group_abstract = [0,4,17,18,35,36,37,38,39,40,41,42,43,70,71, 78]
group_tribal = [72,73,74,75,76,77,78]
group_0 = group_abstract
group_1 = group_abstract + group_main_american
group_2 = group_abstract + group_main_american + group_tribal + group_minor + group_religious
group_3 = group_abstract + group_main_american + group_tribal + group_minor + group_religious + group_sexual
groups = [('0',group_0),('1',group_1),('2',group_2),('3',group_3),]
blackball_list = list(range(22))
#####################
# ALGORITHM FUNCTION
#####################
def rr_detector(title_raw, abstract_raw):
title = title_raw.lower()
abstract = abstract_raw.lower()
result_dict = {}
for g in search_regexs_groups:
gname = g[0][0]
any_ = 0
any_ex_last = 0
group_data = {}
for group, code, search_regex in g:
abstract_matches = search_regex.findall(abstract)
abstract_count = len(abstract_matches)
abstract_last_matches = search_regex.findall(abstract.split('. ')[-1])
abstract_last_count = len(abstract_last_matches)
title_matches = search_regex.findall(title)
title_count = len(title_matches)
any_ += abstract_count + title_count
any_ex_last += abstract_count + title_count - abstract_last_count
group_data['_'.join([group, str(code), 't', 'c'])] = title_count
group_data['_'.join([group, str(code), 'at', 'c'])] = abstract_count + title_count
group_data['_'.join([group, str(code), 'at_ex_last', 'c'])] = abstract_count + title_count - abstract_last_count
group_data['_'.join([group, str(code), 'at', 'm'])] = int(abstract_count + title_count > 0)
group_data['_'.join([group, str(code), 'at', 'match'])] = ';'.join(title_matches + abstract_matches)
group_data['_'.join([gname, 's', 'at', 'm'])] = int(any_ > 0)
group_data['_'.join([gname, 's', 'at', 'c'])] = any_
group_data['_'.join([gname, 's', 'at_ex_last', 'm'])] = int(any_ex_last > 0)
group_data['_'.join([gname, 's', 'at_ex_last', 'c'])] = any_ex_last
result_dict.update(group_data)
#result_dict[gname] = group_data
results = {}
results['blackballed'] = 0
for gname, gvalue in result_dict.items():
if 'blackball' in gname and '_at_c' in gname and 's' not in gname:
if gvalue > 0 and results.get('blackballed', 0) != 1:
results['blackballed'] = 1
result_dict.update(results)
results = {}
for e, l in groups:
results[f'group_{int(e)}'] = 0
for i in l:
if result_dict[f'ethnicity_{int(i)}_at_ex_last_c'] > 0 and results.get(f'group_{int(e)}', 0) != 1:
results[f'group_{int(e)}'] = 1
result_dict.update(results)
results = {}
for e,l in groups:
results[f'match_{int(e)}'] = 0
for i in l:
if result_dict[f'ethnicity_{int(i)}_t_c'] > 0:
if result_dict[f'group_{int(e)}']*result_dict['issue_s_at_m'] > 0 and results.get(f'match_{int(e)}', 0) != 1:
results[f'match_{int(e)}'] = 1
result_dict.update(results)
results = {}
for e,l in groups:
results[f'match_{int(e)}'] = (1-result_dict['blackballed'])*result_dict[f'match_{int(e)}']
result_dict.update(results)
#DETAILES ANALYSIS
#Extract keywords (issue words)
results_issue = []
for gname, gvalue in result_dict.items():
if 'issue_' in gname and 'at_match' in gname:
results_issue.append(gvalue)
unique_issue = set()
for item in results_issue:
unique_issue.update(item.split(';'))
issue_count = {} #<-- dictionary with unique issue keywords
for term in unique_issue:
count = 0
for item in results_issue:
count += item.count(term)
issue_count[term] = count
if '' in issue_count:
del issue_count['']
#Extract group (ethnic words)
results_group = []
for gname, gvalue in result_dict.items():
if 'ethnicity_' in gname and 'at_match' in gname:
results_group.append(gvalue)
unique_group = set()
for item in results_group:
unique_group.update(item.split(';'))
group_count = {} #<-- dictionary with unique group keywords
for term in unique_group:
count = 0
for item in results_group:
count += item.count(term)
group_count[term] = count
if '' in group_count:
del group_count['']
#Extract blackball
results_blackball = []
for gname, gvalue in result_dict.items():
if 'blackball_' in gname and 'at_match' in gname:
results_blackball.append(gvalue)
unique_blackball = set()
for item in results_blackball:
unique_blackball.update(item.split(';'))
blackball_count = {} #<-- dictionary with blackball keywords
for term in unique_blackball:
count = 0
for item in results_blackball:
count += item.count(term)
blackball_count[term] = count
if '' in blackball_count:
del blackball_count['']
#Append dictionaries into a data frame for Detailed statistics
df_issue = pd.DataFrame(list(issue_count.items()), columns=['term', 'freq'])
df_issue['type'] = 'ISSUE'
df_issue = df_issue[['type', 'term', 'freq']]
df_group = pd.DataFrame(list(group_count.items()), columns=['term', 'freq'])
df_group['type'] = 'GROUP'
df_group = df_group[['type', 'term', 'freq']]
df_blackball = pd.DataFrame(list(blackball_count.items()), columns=['term', 'freq'])
df_blackball['type'] = 'WHITELIST'
df_blackball = df_blackball[['type', 'term', 'freq']]
df_details = pd.concat([df_group, df_issue, df_blackball], ignore_index=True)
issue_default = {'type': 'ISSUE', 'term': '', 'freq': ''}
group_default = {'type': 'GROUP', 'term': '', 'freq': ''}
blackball_default = {'type': 'WHITELIST', 'term': '', 'freq': ''}
df_details.loc[len(df_details)] = issue_default
df_details.loc[len(df_details)] = group_default
df_details.loc[len(df_details)] = blackball_default
df_details = df_details.sort_values(by='type', ascending=False)
#TEXT ANALYSIS
#Dictionary with issue, topic, and blackball keywords
keywords_dict = {"issue": [], "group": [], "whitelist": []}
keywords_dict["issue"].extend(issue_count.keys())
keywords_dict["group"].extend(group_count.keys())
keywords_dict["whitelist"].extend(blackball_count.keys())
combined_text = f"TITLE:\n{title_raw} \n \nABSTRACT:\n{abstract_raw}"
keywords = [(word, key, len(word)) for key, words in keywords_dict.items() for word in words]
keywords = sorted(keywords, key=lambda x: -x[2])
if len(keywords) > 0:
pattern = re.compile("|".join(map(re.escape, [x[0] for x in keywords])), re.IGNORECASE)
matches = re.finditer(pattern, combined_text)
text_analysis = []
last_end = 0
for match in matches:
start = match.start()
end = match.end()
if start != last_end:
text_analysis.append((combined_text[last_end:start], None))
for keyword, key, length in keywords:
if re.match(re.escape(keyword), match.group(), re.IGNORECASE):
text_analysis.append((combined_text[start:end], key))
break
last_end = end
if last_end != len(combined_text):
text_analysis.append((combined_text[last_end:], None))
else:
text_analysis = [(combined_text, None)]
#FORM THE MAIN OUTPUT
#Output
if result_dict['match_1'] == 1:
#Result
output_image = os.path.join(dirname, 'images/yes.png')
#Explanation
unique_group_str = ', '.join(unique_group)
unique_issue_str = ', '.join(unique_issue)
answer = "This paper can be considered race-related, as it mentions at least one group keyword AND one topic keywords in title or abstract. Furthermore, the algorithm does not identify any blackball phrases in the title and abstract provided."
else:
if len(blackball_count) > 0:
#Result
output_image = os.path.join(dirname, 'images/no.png')
#Explanation
unique_blackball_str = ', '.join(blackball_count)
answer = "This paper cannot be considered race-related, as it includes the whitelist phrase(s), such as: " + unique_blackball_str + "."
else:
#Result
output_image = os.path.join(dirname, 'images/no.png')
#Explanation
answer = "This paper cannot be considered race-related, as it does not mention at least one group AND one topic keywords in title or abstract, or it does mention group keywords but only in the last sentence of provided abstract."
#Details
if len(issue_count.keys()) == 0 and len(group_count.keys()) == 0 and len(blackball_count.keys()) == 0 :
data = {
"type": ["WHITELIST", "ISSUE", "GROUP"],
"term": ["term1", "term2", "term3"],
"freq": [0, 0, 0]
}
df_details = pd.DataFrame(data)
if len(abstract_raw) == 0:
output_image = os.path.join(dirname, 'images/default.png')
answer = "We need more information. Please submit abstact."
if len(title_raw) == 0:
output_image = os.path.join(dirname, 'images/default.png')
answer = "We need more information. Please submit title."
if len(title_raw) == 0 and len(abstract_raw) == 0:
output_image = os.path.join(dirname, 'images/default.png')
answer = "We need more information. Please submit title and abstract."
return(output_image, answer, df_details, text_analysis)
#####################
# STYLE
#####################
def_image = os.path.join(dirname, 'images/default.png')
title_prompt = """
<!DOCTYPE html>
<html>
<head>
<style>
.title {
font-family: Arial, sans-serif;
font-size: 32px;
font-weight: bold;
text-align: center;
letter-spacing: 3px;
color: #333;
}
.subtitle {
font-family: Arial, sans-serif;
font-size: 18px;
text-align: center;
color: #666;
}
</style>
</head>
<body>
<div class="title">IDENTIFYING RACE-RELATED RESEARCH</div>
</body>
</html>
"""
description_prompt = """
<p>This app is supplementary material to the <strong>"Race-related Research in Economics" paper</strong>, where we examine how academic economists contribute to discussions about racial justice and enduring economic disparities among different racial and ethnic groups. Specifically, we analyze the production of race-related research in Economics. Our study is based on the analysis of a corpus of 250,000 economics publications from 1960 to 2020, employing an algorithmic approach to classify race-related publications. <strong>This app enables users to verify whether their research can be categorized as race-related based on our algorithm</strong>.</p>
<p>If you would like our algorithm to classify your research, please submit the title and abstract of your paper. By default, the title and abstract of our paper are provided, and you can verify whether it is a race-related research.</p>
"""
#####################
# APP LAUNCH
#####################
title_smpl = "Race-related Research in Economics"
abstract_smpl = "Issues of racial justice and persistent economic inequalities across racial and ethnic groups have risen to the top of public debate. The ability of academic economists to contribute to these debates in part depends on the production of race-related research in the profession. We study the issue combining information on a corpus of 250,000 publications in economics from 1960 to 2020 on which we use an algorithmic approach to classify race-related publications, constructing paths to publication for 22,000 NBER working papers between 1974 and 2015, and constructing the career prole of publications of 2800 economics faculty in US economics departments active in 2020/1. We present four new stylized facts on race-related research in economics. First, since 1960 less than 2% of publications in economics have been race related, with an uptick in such work since the mid 1990s. This represents a cumulative body of knowledge of 3801 race-related publications in economics since 1960. Second, the publications process provides little disincentive to produce race-related research: such work has similar or better publication outcomes as non race-related research. Third, Black faculty are significantly more likely to publish race-related work during their career. However, citations and H-indices are significantly lower for minority faculty as a whole. However, the citation penalty for Black faculty is partially offset for their race-related publications. Fourth, over later stages of the career life cycle, Black faculty become less likely to work on race-related topics. The timing of this change coincides with their career progression up the ranking of US academic departments. We draw together policy implications for the profession related to innovative areas of race-related research that economists can engage in, and processes to improve the selection and retention of minority faculty."
demo = gr.Interface(fn=rr_detector, inputs=[
gr.Textbox(label="Title", value=title_smpl, lines=1),
gr.Textbox(label="Abstract", value=abstract_smpl, lines=18)],
outputs=[
gr.Image(label = 'Result', value=def_image),
gr.Textbox(label="Explanation"),
gr.BarPlot(
label = "Details",
x="term",
y="freq",
color="type",
title="Frequency of Race-related Keywords in Title and Abstract",
tooltip=["type", "term", "freq"],
vertical=False,
#caption = "TEST",
height = 150,
width = 300,
color_legend_title = 'Type of Keywords',
x_title = "Keywords",
y_title = "Frequency",
show_label = True,
#sort = '-x',
color_legend_position = 'right',
),
gr.HighlightedText(
label="Text Analysis",
color_map = {'group': 'blue', 'issue': 'green', 'whitelist': 'red'}
),
], theme='Jameswiller/Globe', title = title_prompt, description = description_prompt, allow_flagging = 'auto')
#theme='gradio/monochrome'
#theme='ParityError/Interstellar'
if __name__ == "__main__":
demo.launch(share=True)
'''
1. Double count when two word keywords
Title: Race inequality as a concept
Abstract: This paper is about race inequality.
2. The original algo (mis)classify this as RR, why?
ID: wos_rbpe_1032
Title: residential location and the earnings of african american women
Abstract: in comparing the earnings of african american women to three reference groupswhite women african american men and white menthree principal findings emerge. first african american women residing in the suburbs are worse off than any other suburban group. second central city african american women are worse off than any other group of central city residents. third while central city residence imposes a statistically significant earnings penalty on men of both races no such penalty is found for african american or white women. therefore african american women will enjoy no earnings advantage if they move to the suburbs. this finding underscores the importance of including women in studies of residential location and the socioeconomic status of african americans. a narrow focus on male data to inform policy is clearly insufficient. © 1995 springer. all rights reserved.
''' |