Bibliographic record
Abstract
Several organizations and individuals made available materials that were essential to this research.A number of feminist and partisan activists in both the United States and Canada agreed to be interviewed for this research and were generous with their time and personal files.Both the Liberal and the New Democratic Parties of Canada granted permission to use closed archival collections that yielded valuable information, provided party documents, and made it possible to attend party conventions as an observer.In addition, the staff of the Canadian Women's Movement Archives at the University of Ottawa and at the National Archives of Canada were very patient with a novice archival researcher.Data for Canadian party conventions were made available by George Perlin of the Centre for the Study of Public Opinion at Queen's University, Keith Archer of the University of Calgary, and Alan Whitehorn of Royal Military College.Data for American party conventions were made available by Denise Baer and by the Inter-University Consortium for Political and Social Research at the University of Michigan.Neither the collectors of the data nor the ICPSR bear any responsibility for the analyses or interpretations presented here.A special note of thanks to Laine Reuss of the University of Toronto Data Library, who was of great assistance in procuring the American data sets, and to Joanna Everitt, who was extraordinarily patient in her explanation of matters statistical.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.551 | 0.296 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".