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Record W2093419586 · doi:10.4000/ejas.10502

Feminist Interventions in Political Representation in the United States and Canada: Training Programs and Legal Quotas

2015· article· en· W2093419586 on OpenAlexaffabout
Chantal Maillé

Bibliographic record

VenueEuropean Journal of American Studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsConcordia University
Fundersnot available
KeywordsPromotion (chess)PoliticsRepresentation (politics)MobilizationPolitical sciencePsychological interventionPerspective (graphical)Intervention (counseling)Public administrationPolitical mobilizationTraining (meteorology)Political economyPublic relationsEconomic growthLawSociologyEconomicsPsychology

Abstract

fetched live from OpenAlex

While many countries have adopted quota laws to regulate the election of women to political office, the United States and Canada seem unaffected by this trend. In this article, I seek an explanation for this and examine the role of women's movements and some of the initiatives launched over the last 25 years to counter the problem of low numbers of elected women in Canadian and American parliaments. I examine features common to the approaches of American and Canadian women's movements, both of which are characterized by a strong emphasis on training for political office and an absence of mobilization in favor of legal quotas. Women's groups involved in the promotion of women in politics in the U.S. and Canada do not support the strategy of legal quota implementation; rather, one type of intervention is favored over all others: training programs. I conclude that the absence of campaigns for legal quotas in Canada and the United States can be linked to the lack of mobilization for quotas on the part of women's organizations. However, from a feminist perspective, training programs for women who want to run for office are grounded in problematic assumptions.

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 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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score0.570

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 studies0.0000.001
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 head0.205
GPT teacher head0.408
Teacher spread0.204 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2015
Admission routes2
Has abstractyes

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