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Record W2793266328 · doi:10.1017/s0008423917001421

In Crisis or Decline? Selecting Women to Lead Provincial Parties in Government

2018· article· en· W2793266328 on OpenAlexaffabout
Melanee Thomas

Bibliographic record

VenueCanadian Journal of Political Science · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGovernment (linguistics)Political sciencePoliticsPopulationGovernment OfficePolitical economyPublic administrationEconomicsSociologyDemographyLocal governmentLaw

Abstract

fetched live from OpenAlex

Abstract The majority of Canada's women premiers were selected to that office while their parties held government. This is uncommon, both in the comparative literature and among premiers who are men. What explains this gendered selection pattern to Canada's provincial premiers’ offices? This paper explores the most common explanation found in the comparative literature for women's emergence as leaders of electorally competitive parties and as chief political executives: women are more likely to be selected when that party is in crisis or decline. Using the population of women provincial premiers in Canada as case studies, evidence suggests three of eight women premiers were selected to lead parties in government that were in crisis or decline; a fourth was selected to lead a small, left-leaning party as predicted by the literature. However, for half of the women premiers, evidence of their party's decline is partial or inconclusive. As a result of this exploration, more research is required to draw generalizations about the gendered opportunity structures that shape how women enter (and exit) the premier's office in Canada.

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)0.001
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 score0.195
Threshold uncertainty score0.393

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.002
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.040
GPT teacher head0.353
Teacher spread0.313 · 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

Citations35
Published2018
Admission routes2
Has abstractyes

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