Bibliographic record
Abstract
Contributors Acknowledgments Gendering the Nation State: An Introduction / Yasmeen Abu-Laban Part 1: Gender and Nation 1. Gendering the Hyphen: Gender Dimensions of Modern Nation-State Formation in Euro-American and Anti- and Post-Colonial Contexts / Jill Vickers 2. Gender and Nation in the Soviet/Russian Transformation / Maya Eichler 3. Projecting Gender and Nation: Literature for Immigrants in Canada and Sweden / Shauna Wilton Part 2: Gender and State Processes 4. Assembling Women, Gendering Assemblies / Linda Trimble 5. Feminist Ideals versus Bureaucratic Norms: The Case of Feminist Researchers and the Royal Commission on Reproductive Technologies / Francesca Scala 6. Framing Feminists: Market Populism and Its Impact on Public Policy in Australia and Canada / Marian Sawer 7. Women's Rights and Religious Opposition: the Politics of Gender at the International Criminal Court / Louise Chappell Part 3: Gender and Citizenship 8. Putting Gender Back In: Women and Social Policy Reform in Canada / Janine Brodie 9. Citizenship in the Era of New Social Risks. What Happened to Gender Inequalities? / Jane Jenson 10. Carefair: Gendering Citizenship Neoliberal Style / Paul Kershaw 11. Republican Liberty, Naming Laws, and the Role of Patronymy in Constituting Women's Citizenship in Canada and Quebec / Jackie F. Steele 12. Gendering Nation States and/or Gendering City-States: Debates about the Nature of Citizenship / Caroline Andrew Afterword: The Future of Feminism / Judy Rebick Notes Bibliography 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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.020 |
| Science and technology studies | 0.045 | 0.027 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".