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Record W2127322932 · doi:10.1017/s1537592709992684

What do Women Really Know? A Gendered Analysis of Varieties of Political Knowledge

2010· article· en· W2127322932 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenuePerspectives on Politics · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsMcGill University
Fundersnot available
KeywordsPoliticsExpansiveGovernment (linguistics)Political sciencePublic relationsSociologyLaw

Abstract

fetched live from OpenAlex

While studies typically find that women know less about politics than do men, feminist scholars have argued that these findings reflect gender-biased measures that underestimate women's political knowledge. This article evaluates the feminist critique by taking a more expansive view of what constitutes political knowledge. Using data from a large Canadian urban sample, we show that gender gaps close or even reverse when people are queried about more practical aspects of political knowledge, such as government benefits and services. Our results also demonstrate that this type of knowledge is more equally distributed than its conventional counterpart, though the women who are the most likely to need government services and benefits are often the least likely to know about them. Finally, we show that knowledge of government services and benefits has a significant effect on women's intended vote choice. This article thus shows that more practical types of political knowledge might serve as meaningful additions to existing definitions and measures of political knowledge.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.628
Threshold uncertainty score0.539

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.025
GPT teacher head0.357
Teacher spread0.331 · 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