Feminist Interventions in Political Representation in the United States and Canada: Training Programs and Legal Quotas
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.013 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".