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Record W4280620206 · doi:10.1017/s1743923x22000149

Gender Is Not a Proxy: Race and Intersectionality in Legislative Recruitment

2022· article· en· W4280620206 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.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenuePolitics & Gender · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsCarleton University
FundersUniversity of TorontoCanada Research Chairs
KeywordsIntersectionalityLegislatureProxy (statistics)PoliticsRace (biology)Representation (politics)White (mutation)Political scienceGender studiesSociologyLaw

Abstract

fetched live from OpenAlex

Abstract Election to office is shaped by a series of decisions made by prospective candidates, parties, and voters. These choices determine who emerges and is ultimately selected to run, and each decision point either expands or limits the possibilities for more diverse representation. Studies of women candidates have established an important theoretical and empirical basis for understanding legislative recruitment. This study asks how these patterns differ when race and intersectionality are integrated into the analyses. Focusing on more than 800 political aspirants in Canada, I show that although white and racialized women aspire to political office at roughly the same rates, their experiences diverge at the point of party selection. White men remain the preferred candidates, and parties’ efforts to diversify politics have mostly benefited white women. I argue that a greater emphasis on the electoral trajectories of racialized women and men is needed.

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)0.001
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.327
Threshold uncertainty score0.868

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.209
GPT teacher head0.411
Teacher spread0.202 · 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