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Record W2120865610 · doi:10.24124/c677/2012241

Women’s Access to Cabinets in Canada: Assessing the Role of Some Institutional Variables

2013· article· en· W2120865610 on OpenAlexaffvenueabout
Manon Tremblay

Bibliographic record

VenueCanadian Political Science Review · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCaucusCabinet (room)Government (linguistics)PoliticsPolitical sciencePublic administrationFederal electionDemographic economicsEconomicsLawGeography

Abstract

fetched live from OpenAlex

Only recently have women been recruited to serve in Canadian cabinets, and their presence in these bodies remains marginal, although it is progressing steadily. This article has the objective to examine the role of some institutional variables on women’s access to federal and provincial cabinets in Canada, from 1984 to the end of 2007. Six hypotheses are tested exploring the following independent variables: the overall proportion of female legislators versus the proportion of women within the government caucus only; the region; the majority or minority status of the government; the change (or lack of change) of government following a general election; the size of the cabinet; and, the political party that forms the government. The overall pro-portion of women legislators and notably, their proportion within the government caucus both exert an almost monopolistic influence on the feminization rate of cabinets. In addition, the results invite to qualify the idea which suggests that the higher a political role, the harder it is for women to attain.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.437

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0050.002
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.044
GPT teacher head0.351
Teacher spread0.307 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2013
Admission routes3
Has abstractyes

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