Stalled : the representation of women in Canadian governments
Bibliographic record
Abstract
Foreword - Women, Power, Politics: Surveying the Canadian Landscape / Sylvia Bashevkin Introduction: The Road to Gender Parity / Manon Tremblay, Jane Arscott, and Linda Trimble 1 Truly More Accessible to Women than the Legislature? Women in Municipal Politics / Manon Tremblay and Anne Mevellec 2 The Alberta Advantage? Women in Alberta Politics / Brenda O'Neill 3 When Numerical Gains Are Not Enough: Women in British Columbia Politics / Jocelyne Praud 4 Complacency and Gender Silence: Women in Manitoba Politics / Shannon Sampert 5 A Province at the Back of the Pack: Women in New Brunswick Politics / Joanna Everitt 6 A Laggard No More? Women in Newfoundland and Labrador Politics / Amanda Bittner and Elizabeth Goodyear-Grant 7 Electoral Breakthrough: Women in Nova Scotia Politics / Louise Carbert and Naomi Black 8 Breaking the Holding Pattern? Women in Ontario Politics / Tracey Raney 9 Getting Women's Names on the Ballot: Women in Prince Edward Island Politics / John Crossley 10 Hitting a Glass Ceiling? Women in Quebec Politics / Manon Tremblay 11 A Prairie Plateau: Women in Saskatchewan Politics / Loleen Berdahl 12 In the Presence of Northern Aboriginal Women? Women in Territorial Politics / Graham White 13 Slow to Change: Women in the House of Commons / Lisa Young 14 Way Past That Era Now? Women in the Canadian Senate / Stephanie Mullen, with the collaboration of Manon Tremblay and Linda Trimble Conclusion: A Few More Women / Linda Trimble, Manon Tremblay, and Jane Arscott Index
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.024 |
| Science and technology studies | 0.047 | 0.008 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.002 |
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".