Gendering the state in the age of globalization : women's movements and state feminism in postindustrial democracies
Bibliographic record
Abstract
Chapter 1: Introduction: Women's Movements and State Restructuring in the 1990s Chapter 2: Re-evaluating the Heart of Society: Family Policy in Austria Chapter 3: Feminism and Indigenous Rights in Australia in the 1990s Chapter 4: Speedy Belgians: The New Nationality Law of 2000 and the Impact of the Women's Movement Chapter 5: New Federalism and Cracked Pillars: The Canadian Health Insurance System under the 2000 Romanow Commission and Beyond Chapter 6: Debating Day Care in Finland in the Midst of an Economic Recession and Welfare State Down-Sizing Chapter 7: Thirty-five Hour Workweek Reforms in France, 1997-2000: Strong Feminist Demands, Elite Apathy, and Disappointing Outcomes Chapter 8: Women, Embryos, and the Good Society: Gendering the Bioethics Debate in Germany Chapter 9: The Reform of the State in Italy Chapter 10: Electoral Reform in Mid-1990s Japan Chapter 11: The Home Care Gap: Neoliberalism, Feminism, and the State in the Netherlands Chapter 12: The Women's Movement, State Feminism, and Unemployment Reform in Spain, 2002-2003 Chapter 13: The Debate about Care Allowance in the Light of Welfare State Reconfiguration Chapter 14: The UK: Reforming the House of Lords Chapter 15: Welfare Reform: America's Hot Issue Chapter 16: Conclusion: State Feminism and State Restructuring since the 1990s
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.030 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".