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Record W4244645105 · doi:10.1177/1065912919870605

Discursive Inequity and the Internal Exclusion of Women Speakers

2019· article· en· W4244645105 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

VenuePolitical Research Quarterly · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsMcGill University
Fundersnot available
KeywordsPoliticsIdeal (ethics)Public sphereInequalitySociologyGender studiesPolitical scienceSocial psychologyLawPsychology

Abstract

fetched live from OpenAlex

In today’s democracies, disempowered group members are no longer formally barred from the political arena. However, there is a concern that the historical memory of political inequality and exclusion remains as internalized cognitive dispositions, shaping behavior even after laws are changed. Focusing on the legacy of women’s political exclusion from the public sphere, I consider whether internal exclusions undermine women’s ability to influence political discourse even under conditions of formal political equality. All else being equal, do women and men in Western democracies have the same discursive influence? Are women particularly sensitive to men’s discursive authority? I help answer these questions using an experimental research design. The results of my study offer evidence that people are more willing to revise their opinions after hearing a man’s counterargument than after hearing a woman’s identical counterargument. This pattern appears to be driven by the way women respond to a man’s counterclaim. I discuss how gendered discursive inequities reinforce existing patriarchal structures, and the role that women inadvertently play in their own subjugation. I conclude by offering suggestions for better approximating the ideal of discursive gender equality.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.237
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.055
GPT teacher head0.427
Teacher spread0.372 · 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