Bibliographic record
Abstract
Figures and Tables Acknowledgments Abbreviations 1 Introduction / Sylvia Bashevkin Part 1: Community and Women's Group Participation 2 Women and Community Leadership: Changing Politics or Changed by Politics? / Caroline Andrew 3 Rebuilding the House of Canadian Feminism: NAC and the Racial Politics of Participation / Mary-Jo Nadeau Part 2: Winning Legislative Seats 4 Women in the Quebec National Assembly: Why So Many? / Manon Tremblay, with Stephanie Mullen 5 Are Cities More Congenial? Tracking the Rural Deficit in the House of Commons / Louise Carbert Part 3: Cabinet and Party Leadership Experiences 6 Making a Difference When the Doors Are Open? Women in the Ontario NDP Cabinet, 1990-95 / Lesley Byrne 7 Stage versus Actor Barriers to Women's Federal Party Leadership / Sylvia Bashevkin 8 One Is Not Like the Others: Allison Brewer's Leadership of the New Brunswick NDP / Joanna Everitt and Michael Camp Part 4: Media and Public Images 9 Crafting a Public Image: Women MPs and the Dynamics of Media Coverage / Elizabeth Goodyear-Grant 10 Do Voters Stereotype Female Party Leaders? Evidence from Canada and New Zealand / Elisabeth Gidengil, Joanna Everitt, and Susan Banducci Part 5: Remedies and Prescriptions 11 Opening Doors to Women's Participation / Sylvia Bashevkin Contributors Index
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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.030 |
| Science and technology studies | 0.033 | 0.004 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 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".