185:, I was surprised by the low number of women among the people cited in the article. So I've started exploring methods to measure gender diversity. I draw a distinction between gender diversity and gender parity. First, gender parity supposes binary gender, which excludes non-binary people. Second, gender parity implies that the ideal would be a fifty-fifty divide between men and women. After some iterations, I've found a way to measure gender diversity at the article level. This tool can be used to explore gender diversity for articles about academic fields, activities, or occupations. My approach is very basic and simply computes the share of people cited in an article by gender.
711:"Nobody would be able to say what a fair share of females in the article would be. However, I personally think that 5% is not much and that the contribution of women to economics is more important." This is kind of a cop-out. I'd like to know what the state of the representation of women is in the world of possible citations before adjucating whether we are below this line. Are 5% of academic papers in the field of economics published by women? I don't know, but this would be helpful information in determining whether or not the above proposal is meant to align our citations with academia due to some unconscious bias on the part of editors against women or is some sort of
238:
847:
multiplying two disparities together. For example, an article has x% of citations from males and y% of citations from females. Now, for the sake of simplicity, lets say 80% of all biographical articles are about males and 19% are about females. Only comparing citations with linked articles we have x*0.8 and y*0.19. This results in a far lower percentage for female citations in the graphs than is mentioned in the article. I am not sure how we improve the calculation methodology but it is worth remembering that the level of the imbalance reported is distorted by our own distorted data.
296:
824:). Of course we need to rely on sources and reflect the reality of the topic. But we have some editorial freedom in the way we write articles and we can develop some aspects of the topic. In the article about economics in French, I've dedicated a section to the question of women in economics. I think it's a good way to start (if there are some sources of course). Last but not least, it's also in my opinion one aspect of the concept of "knowledge equity", which is key in Wikimedia movement strategy (
372:, I find that all of them have a proportion of men higher than 80%. Values for computer science and political science should be taken with caution since the number of people cited in those articles is lower than 50. If we exclude computer science and political science, we find that 10 out of 15 articles have less than 10% of women among all gendered entities! If we look at raw numbers, the count of women in each article is really low: 4 women in mathematics, 4 women in medicine, 1 woman in physics.
110:
957:
while on sabbatical in France and having an affair with the musician ]", where the bluelink has little to do with the topic at hand. "Mentioned and linked" might be better if you truly think "wikilinked" is too jargon-y. As a side note, I'd argue that an attempt to do this same test but for references / Bibliographies only would be a worthy endeavor, just some articles don't have well-formatted citations, and you can't look at
Wikidata for unlinked authors.
130:
529:
90:
1118:
fix wiki-wide problems but seem to begrudge people giving up their own time to discuss how we can better understand what the problem is and where we should fix it. That you wanted to improve the economics article and went ahead and edited it is great. However, you shouldn't expect every editor to conform to your expectations and timescales. We all improve the project in our own ways and at our own speed.
120:
36:
140:
100:
150:
1117:
You are also misrepresenting this discussion. While a few people here have talked about the example used of the economics article, most of the comments are about the principles and methods of analysis. Is there actually a problem and is the data a valid representation of the situation? You want us to
978:
Yes, the tool cannot presently look at
Wikidata for authors that do not have a link from the analyzed Knowledge (XXG) article, but many scholarly publications are in Wikidata, many of their authors have been disambiguated, and still a sizeable number of these have gender information, so by looking up
219:
This is a very basic approach. It doesn't distinguish any difference between entities cited in the references and entities cited in the core of the article. It doesn't take into account people cited in the article without a link to a
Knowledge (XXG) article. But even if it's imperfect, I believe this
1088:
Perhaps action is more important than discussion - do we wait to see if anyone else actually adds the other 2 I mentioned? Maybe then we can do an analysis of why no one bothered to actually fix the thing you were all discussing? I will leave it up to one of the other nine or so editors to maybe add
1015:
Although it is noted that there is indeed a disparity, that is mainly because women were not entitled to study at degree level until the 20th century. As such, there were very few women who COULD be mentioned on such a wide topic as "Economics", and they would be massively outnumbered prior to 1940.
819:
We face the same issue with the share of women among biographies. No one know what is the good or fair share (15%, 19%, 30%?). But in the last years, projects such as Women in Red have focused on this issue and made an effort to increase the number of biographies dedicated to women. I'm just raising
172:
When thinking about gender diversity in
Knowledge (XXG), we often think of the number of biographical articles about men and women. The Humaniki project shows that about 19% of biographical articles on the English Knowledge (XXG) are about women. However, this is only one aspect of gender diversity.
1113:
So, you are complaining about knee jerk reactions but want 8+ editors to jump in and attempt to fix something they may not be familiar with? My interest and expertise do not lie with economics, so you are better placed than I to look at that article. Also, your example of a set of bad edits involve
919:
You say in the text that you're measuring the "share of people cited in an article by gender" (later you refer to the percentage of "people quoted in the article"). I think most readers would understand this to mean that you're looking at the gender distribution of the authors of works cited in the
205:
in
Wikidata). Note that gender in Wikidata can be male, female, non-binary, intersex, transgender female, transgender male, or agender. I'd find it more intuitive to group together transgender males with males and transgender females with females but I prefer to keep the classification of Wikidata.
303:
In this section, I compare gender diversity in
Knowledge (XXG) articles about some important academic fields. As with economics, we know that most academic fields have long been dominated by male figures. So we're not surprised to find a relative low share of women in Knowledge (XXG) articles. By
188:
This simple quantitative approach to measure gender diversity is similar to many research projects on this theme in computational social sciences. David
Doukhan is tracking women's speaking time on the radio. Antoine Mazières and his co-authors are computing the share of screen time with women in
956:
Even if the term "wikilinked" isn't perfect, "cited" is worse. It means it won't count a female author who either isn't mentioned, isn't linked, or is red linked in the references section, but will count a bluelink that says something like "Economist John Doe wrote his seminal work on economics
846:
I am a bit wary of the methodology here. As acknowledged in the article above, only those people with
Knowledge (XXG) articles get counted in the statistics. If we go with the opening premise that non-male genders are under represented in Knowledge (XXG) articles, we are compounding the error by
1079:
I have no fore-knowledge, except A level economics - I simply did a search on Google for the top 10 female economists, read about them, and used that info. That should have already been done, since this page has discussion involving 8+ editors going back for at least two weeks. I feel that is a
1054:
The analysis has drawn to your attention (someone who has knowledge of female involvement in
Economics) that there is a gap in the article and you have made an improvement to it. I would say that is a positive. Similar analysis of other articles may help identify other areas where there are
387:
In the next months, I would like to explore gender diversity in articles about occupations (journalist, politician, etc.) and activities (journalism, politics, sports, etc.). I would also like to have large scale studies looking at all articles about academic fields or all articles about an
254:
are male. So no one is really surprised to find a vast majority of males in the results. Nobody would be able to say what a fair share of females in the article would be. However, I personally think that 5% is not much and that the contribution of women to economics is more important.
391:
My experiments with measuring gender diversity in
Knowledge (XXG) articles lead me to believe that women are often forgotten or undermined in Knowledge (XXG) articles about general topics. It would be worthwhile to give specific attention to this topic. WikiProjects such as
412:"The idea of closing the “gender gap” itself has always struck me as somewhat problematic as it implies a gulf between two equivalent sides and reinforces the idea of binary gender. An aspiration to equitable “gender diversity” might be more fitting" writes
245:
Let's have a look at the article about economics. In May 2022, we find 137 males, 6 cisgender females, and 1 transgender female. So fewer than 5% of people quoted in the article are female. Of course, everyone knows that many prominent economists from
1089:
some detail on the ladies I mentioned as I feel perhaps there is a litle bit of looking for a disparity rather than curing it. I did not see a "gap in the article", I saw a gap in the editing of said article after someone had raised a flag.
1084:
include removing a male author, instead of leaving him and adding the inserted female one; which actually looks like more of a negative considering that the article now does not include the counter statement to the previous paragraph
1092:... and yes, I get annoyed about things that are discussed and never actually acted upon Wikiwide, as well as hasty knee-jerk editing that tries to correct a perceived wrong but actually lowers the accuracy of an article.
223:
Numbers should be interpreted with caution. The number of gendered entities cited in a single article is often very low. I personally don't interpret proportions if the total number of gendered entities is lower than 50.
825:
892:
This occurred to me as well. In absence of information on any differences between the two proportions (% cited and % bluelinked), root-transforming sounds like a reasonable hack to remove the compound effect.
1028:
to the article, as she co authored a book where her husband was mentioned (but not her!?!). I fear all this analysis & debate is less positive than adding stuff that is seen to be obviously missing
593:
1058:
Most of the "debate" above is about refining the method of analysis to produce more accurate data. With accurate data, we will be able to spot articles that have an unusual disparity and correct them.
795:. I don’t think we need to know detailed statistics about the contribution of women to economics to know that 95% of citations being from men is likely to be unrepresentative and worth improving on. —
653:
638:
598:
588:
603:
623:
618:
633:
628:
578:
563:
558:
76:
864:
you're right. This can be part of the interpretation of the results and one way to improve gender diversity in an article would simply be to create articles about women named in the article.
689:
553:
573:
1114:
an ongoing content dispute on the article talk page that predated the publication of this edition of Signpost. Why are you trying to link an unrelated content dispute to the editors here?
613:
546:
878:
Since the level of representation is being approximately squared by your methodology, perhaps the square root of the result would be a more accurate estimate of the representation. ~
608:
494:
540:
516:
507:
55:
44:
381:
648:
568:
663:
658:
643:
201:. Then I combine this query with a Wikidata SPARQL query. I select all links corresponding to human beings in Wikidata (property P31 is Q5) and I retrieve their gender (
792:
483:
213:
1199:
21:
1175:
1170:
1165:
93:
1160:
495:
https://observablehq.com/@pac02/gender-diversity-in-wikipedia-articles-evidence-from-some?collection=@pac02/gender-diversity-in-wikipedia-articles
1148:
979:
the publications in Wikidata and their gender diversity, a more finegrained picture might emerge for the Knowledge (XXG) article in question.
393:
380:
I believe that measuring helps to raise awareness of the problem of gender diversity in Knowledge (XXG) articles. Anyone can play with the
436:
484:
https://observablehq.com/@pac02/explore-gender-diversity-in-a-single-wikipedia-article?wikipedia=en.wikipedia.org&article=Economics
1155:
528:
49:
35:
17:
757:
237:
905:
791:
It could be argued, though, that “increasing the proportion of women in our citations for the sake of such” is one way of
744:, we're here to report what reliable sources are saying or have said about notable people, incidents, processes, etc. –
189:
popular movies and Gilles Bastin and his co-authors are computing gender frequency of people cited in French newspapers.
1123:
1063:
852:
472:
920:
article and listed in the "References" section. So it was surprising to see that you're actually measuring the people
756:
There does appear to be a significant gender gap in the field of economics itself according to the sources cited in
425:
173:
In this article, I develop a method which measures gender diversity at the article level and show why it's useful.
985:
1181:
1127:
1101:
1067:
1038:
1010:
990:
966:
951:
933:
910:
887:
873:
856:
837:
814:
782:
751:
735:
1119:
1074:
1059:
861:
848:
295:
197:
For each article, I get the list of internal links (also known as blue links). I retrieve them using the
804:
182:
1097:
1034:
1006:
981:
774:
437:
https://larevuedesmedias.ina.fr/la-radio-et-la-tele-les-femmes-parlent-deux-fois-moins-que-les-hommes
1025:
748:
260:
962:
712:
396:
could focus on this issue to ensure that the role of women hasn't been diminished in articles.
103:
929:
901:
728:
685:
345:
256:
123:
798:
321:
30:
Measuring gender diversity in Knowledge (XXG) articles: A new approach at the article level.
741:
133:
1108:
1093:
1049:
1030:
1002:
761:
720:
413:
153:
947:
883:
869:
833:
745:
317:
942:. The term wiki linked is more precise but I'm not sure that everyone understands it.
241:
Chart measuring gender diversity in the Knowledge (XXG) article Economics in May 2022.
1193:
1021:
1017:
973:
958:
808:
448:
272:
268:
264:
212:
Everyone can compute gender diversity for a single Knowledge (XXG) article using the
715:
proposal to increase the proportion of women in our citations for the sake of such.
1024:(who is THE ONLY woman to ever have won a Nobel prize for economics). I have added
939:
925:
895:
309:
280:
276:
143:
426:
https://wikipedia20.pubpub.org/pub/4d61w771/release/2?readingCollection=08ec69da
329:
284:
251:
202:
113:
821:
716:
460:
369:
333:
325:
247:
943:
879:
865:
829:
421:
349:
313:
198:
163:
447:"Computational appraisal of gender representativeness in popular movies",
337:
365:
357:
353:
305:
209:
Last, I count the number of entities by gender and compute the share.
826:
Knowledge (XXG):Wikimedia Strategy 2018–20/Innovate in Free Knowledge
341:
294:
236:
416:
in "Capstone: Making History, Building the Future Together", in
299:
Share of people cited in articles by gender for academic fields
54:
361:
527:
34:
1081:
701:
694:
674:
71:
Measuring gender diversity in Knowledge (XXG) articles
699:If your comment has not appeared here, you can try
1016:I am surprised that there is no mention of either
449:https://www.nature.com/articles/s41599-021-00815-9
287:have all made major contributions to economics.
8:
461:https://gendered-news.imag.fr/genderednews/
820:the same issue at the article level (poke
1200:Knowledge (XXG) Signpost archives 2022-05
1001:Thank you for this interesting article!
18:Knowledge (XXG):Knowledge (XXG) Signpost
702:
678:
405:
183:economics on the French Knowledge (XXG)
70:
29:
7:
760:, though not nearly as low as 5%. ––
1020:(co-edited the Friedman book), nor
181:While working on the article about
56:
28:
684:These comments are automatically
148:
138:
128:
118:
108:
98:
88:
740:Good point. We're not here to
695:add the page to your watchlist
471:See the SPARQL queries in the
214:gender diversity explorer tool
1:
384:and discover some insights.
1216:
382:gender diversity inspector
376:Conclusion and discussion
199:Knowledge (XXG) links API
1128:09:25, 9 June 2022 (UTC)
1102:07:15, 9 June 2022 (UTC)
1068:05:44, 9 June 2022 (UTC)
1039:03:34, 9 June 2022 (UTC)
1011:09:15, 3 June 2022 (UTC)
991:01:43, 5 June 2022 (UTC)
967:23:42, 2 June 2022 (UTC)
952:06:55, 31 May 2022 (UTC)
938:Thanks for the feedback
934:19:50, 30 May 2022 (UTC)
911:07:16, 2 June 2022 (UTC)
888:19:36, 31 May 2022 (UTC)
874:06:55, 31 May 2022 (UTC)
857:17:33, 30 May 2022 (UTC)
838:16:45, 31 May 2022 (UTC)
815:09:22, 30 May 2022 (UTC)
793:countering systemic bias
783:05:11, 30 May 2022 (UTC)
752:02:46, 30 May 2022 (UTC)
736:21:47, 29 May 2022 (UTC)
291:Focus on academic fields
459:Gendered News project,
692:. To follow comments,
532:
300:
242:
220:is a useful approach.
39:
1143:What do you think of
531:
298:
240:
38:
1149:Share your feedback.
1080:negative. Similarly
688:from this article's
418:Knowledge (XXG) @ 20
1026:Mary Paley Marshall
473:project methodology
261:Mary Paley Marshall
1120:From Hill To Shore
1075:From Hill To Shore
1060:From Hill To Shore
924:from the article.
862:From Hill To Shore
849:From Hill To Shore
742:right great wrongs
713:affirmative action
679:Discuss this story
594:WikiProject report
533:
301:
243:
233:Focus on economics
45:← Back to Contents
40:
909:
812:
811:}} me in replies)
734:
703:purging the cache
654:From the archives
639:News from the WMF
599:Technology report
589:Discussion report
346:Political science
257:Harriet Martineau
50:View Latest Issue
1207:
1184:
1112:
1078:
1055:particular gaps.
1053:
989:
977:
899:
898:
813:
802:
801:
781:
779:
771:
770:
732:
724:
706:
704:
698:
677:
604:Featured content
551:
543:
536:
519:
511:
497:
492:
486:
481:
475:
469:
463:
457:
451:
445:
439:
434:
428:
410:
322:Computer science
166:
152:
151:
142:
141:
132:
131:
122:
121:
112:
111:
102:
101:
92:
91:
62:
60:
58:
1215:
1214:
1210:
1209:
1208:
1206:
1205:
1204:
1190:
1189:
1188:
1187:
1186:
1185:
1180:
1178:
1173:
1168:
1163:
1158:
1151:
1140:
1139:
1106:
1072:
1047:
982:Daniel Mietchen
980:
971:
894:
797:
796:
775:
773:
766:
762:
726:
708:
700:
693:
682:
681:
675:+ Add a comment
673:
669:
668:
667:
624:Tips and tricks
619:Recent research
544:
539:
537:
534:
523:
522:
517:
514:
509:
503:
502:
501:
500:
493:
489:
482:
478:
470:
466:
458:
454:
446:
442:
435:
431:
414:Katherine Maher
411:
407:
402:
378:
293:
235:
230:
195:
179:
168:
167:
161:
160:
159:
158:
149:
139:
129:
119:
109:
99:
89:
83:
80:
69:
65:
63:
53:
52:
47:
41:
31:
26:
25:
24:
12:
11:
5:
1213:
1211:
1203:
1202:
1192:
1191:
1179:
1174:
1169:
1164:
1159:
1154:
1153:
1152:
1142:
1141:
1138:
1137:
1136:
1135:
1134:
1133:
1132:
1131:
1130:
1115:
1090:
1086:
1056:
1042:
1041:
1013:
999:
998:
997:
996:
995:
994:
993:
917:
916:
915:
914:
913:
876:
843:
842:
841:
840:
788:
787:
786:
785:
754:
683:
680:
672:
671:
670:
666:
661:
656:
651:
646:
641:
636:
634:News from Diff
631:
629:Traffic report
626:
621:
616:
611:
606:
601:
596:
591:
586:
581:
579:Special report
576:
571:
566:
564:Community view
561:
559:News and notes
556:
550:
538:
526:
525:
524:
515:
506:
505:
504:
499:
498:
487:
476:
464:
452:
440:
429:
404:
403:
401:
398:
377:
374:
318:Social science
292:
289:
234:
231:
229:
226:
194:
191:
178:
175:
170:
169:
157:
156:
146:
136:
126:
116:
106:
96:
85:
84:
81:
75:
74:
73:
72:
67:
66:
64:
61:
48:
43:
42:
33:
32:
27:
15:
14:
13:
10:
9:
6:
4:
3:
2:
1212:
1201:
1198:
1197:
1195:
1183:
1177:
1172:
1167:
1162:
1157:
1150:
1146:
1129:
1125:
1121:
1116:
1110:
1105:
1104:
1103:
1099:
1095:
1091:
1087:
1083:
1076:
1071:
1070:
1069:
1065:
1061:
1057:
1051:
1046:
1045:
1044:
1043:
1040:
1036:
1032:
1027:
1023:
1022:Elinor Ostrom
1019:
1018:Anna Schwartz
1014:
1012:
1008:
1004:
1000:
992:
987:
983:
975:
970:
969:
968:
964:
960:
955:
954:
953:
949:
945:
941:
937:
936:
935:
931:
927:
923:
918:
912:
907:
903:
897:
891:
890:
889:
885:
881:
877:
875:
871:
867:
863:
860:
859:
858:
854:
850:
845:
844:
839:
835:
831:
827:
823:
818:
817:
816:
810:
806:
800:
794:
790:
789:
784:
780:
778:
772:
769:
765:
759:
755:
753:
750:
747:
743:
739:
738:
737:
730:
722:
718:
714:
710:
709:
705:
696:
691:
687:
676:
665:
662:
660:
657:
655:
652:
650:
647:
645:
642:
640:
637:
635:
632:
630:
627:
625:
622:
620:
617:
615:
612:
610:
607:
605:
602:
600:
597:
595:
592:
590:
587:
585:
582:
580:
577:
575:
572:
570:
567:
565:
562:
560:
557:
555:
554:From the team
552:
548:
542:
535:In this issue
530:
521:
513:
496:
491:
488:
485:
480:
477:
474:
468:
465:
462:
456:
453:
450:
444:
441:
438:
433:
430:
427:
423:
419:
415:
409:
406:
399:
397:
395:
389:
385:
383:
375:
373:
371:
367:
363:
359:
355:
351:
347:
343:
339:
335:
331:
327:
323:
319:
315:
311:
307:
297:
290:
288:
286:
282:
278:
274:
273:Anna Schwartz
270:
269:Elinor Ostrom
266:
265:Joan Robinson
262:
258:
253:
249:
239:
232:
227:
225:
221:
217:
215:
210:
207:
204:
200:
192:
190:
186:
184:
176:
174:
165:
155:
147:
145:
137:
135:
127:
125:
117:
115:
107:
105:
97:
95:
87:
86:
78:
59:
51:
46:
37:
23:
19:
1145:The Signpost
1144:
921:
776:
767:
763:
758:this article
725:(please use
583:
574:In the media
547:all comments
520:"In focus" →
490:
479:
467:
455:
443:
432:
417:
408:
394:Women in Red
390:
388:occupation.
386:
379:
310:Architecture
302:
281:Esther Duflo
277:Janet Yellen
244:
222:
218:
211:
208:
203:property P21
196:
187:
180:
171:
94:PDF download
1182:Suggestions
1082:these edits
807:; please {{
799:OwenBlacker
686:transcluded
614:Serendipity
541:29 May 2022
330:Mathematics
285:Susan Athey
252:Jean Tirole
193:Methodology
144:X (Twitter)
57:29 May 2022
1109:Chaosdruid
1094:Chaosdruid
1050:Chaosdruid
1031:Chaosdruid
1003:Zarasophos
922:wikilinked
512:"In focus"
400:References
370:Literature
334:Psychology
326:Philosophy
304:comparing
248:Adam Smith
177:Motivation
82:Share this
77:Contribute
22:2022-05-29
1176:Subscribe
803:(he/him;
746:Athaenara
733:on reply)
690:talk page
609:Interview
422:MIT Press
350:Sociology
314:Economics
1194:Category
1171:Newsroom
1166:Archives
974:SnowFire
959:SnowFire
906:contribs
731:|Chess}}
729:reply to
584:In focus
510:Previous
424:, 2020,
338:Medicine
228:Insights
134:Facebook
124:LinkedIn
114:Mastodon
68:In focus
20: |
940:Colin M
926:Colin M
896:Elmidae
649:Gallery
569:Opinion
366:History
358:Science
354:Biology
306:Physics
764:Formal
664:Humour
368:, and
154:Reddit
104:E-mail
1161:About
822:Chess
717:Chess
659:Essay
644:Video
342:Music
283:, or
16:<
1156:Home
1124:talk
1098:talk
1085:end.
1064:talk
1035:talk
1007:talk
986:talk
963:talk
948:talk
944:PAC2
930:talk
902:talk
884:talk
880:Kvng
870:talk
866:PAC2
853:talk
834:talk
830:PAC2
809:ping
805:Talk
777:talk
768:Dude
721:talk
518:Next
164:PAC2
828:).
362:Art
250:to
162:By
79:—
1196::
1147:?
1126:)
1100:)
1066:)
1037:)
1009:)
988:)
965:)
950:)
932:)
904:·
893:--
886:)
872:)
855:)
836:)
749:✉
727:{{
723:)
508:←
420:,
364:,
360:,
356:,
352:,
348:,
344:,
340:,
336:,
332:,
328:,
324:,
320:,
316:,
312:,
308:,
279:,
275:,
271:,
267:,
263:,
259:,
216:.
1122:(
1111::
1107:@
1096:(
1077::
1073:@
1062:(
1052::
1048:@
1033:(
1005:(
984:(
976::
972:@
961:(
946:(
928:(
908:)
900:(
882:(
868:(
851:(
832:(
719:(
707:.
697:.
549:)
545:(
Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.
