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Record W2975658760 · doi:10.1017/s1049096519001173

How Many Citations to Women Is “Enough”? Estimates of Gender Representation in Political Science

2019· article· en· W2975658760 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePS Political Science & Politics · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Science Research and Education
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPoliticsRepresentation (politics)CitationField (mathematics)Political scienceDistribution (mathematics)Work (physics)Gender biasPublic relationsSocial scienceSociologyPsychologySocial psychologyLaw

Abstract

fetched live from OpenAlex

ABSTRACT Recent studies identified gendered citation gaps in political science journal articles, with male scholars being less likely to cite work by female scholars in comparison to their female peers. Although journal editors, editorial boards, and political scientists are becoming more aware of implicit biases and adopting strategies to remedy them, we know less about the proper baselines for citations in subfields and research areas of political science. Without information about how many women should be cited in a research field, it is difficult to know whether the distribution is biased. Using the gender distribution of membership in professional political science organizations and article authors in 38 political science journals, we provide scholars with suggested minimum baselines for gender representation in citations. We also show that women represent a larger share of organization members than the authors in sponsoring organizations’ journals.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.022
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.438
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0010.009
Scholarly communication0.0010.002
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.081
GPT teacher head0.454
Teacher spread0.373 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it