Gender, Politics and Institutions: Towards a Feminist Institutionalism
Bibliographic record
Abstract
Foreword J.Lovenduski Introduction: Gender, Politics, and Institutions: Setting the Agenda F.Mackay & M.L.Krook Gender and Institutions of Political Recruitment: Candidate Selection in Post-Devolution Scotland M.Kenny Discursive Strategies for Institutional Reform: Gender Quotas in Sweden and France L.Freidenvall & M.L.Krook Gendered Institutions and Women's Substantive Representation: Female Legislators in Argentina and Chile S.Franceschet Gendering the Institutional Reform of the Welfare State: Germany, the United Kingdom, and Switzerland M.Beyeler & C.Annesley Gender and Institutions of Multi-Level Governance: Child Care and Social Policy Debates in Canada J.Grace The Institutional Roots of Post-Communist Family Policy: Comparing the Czech and Slovak Republics H.Haskova & S.Saxonberg Gendering Federalism: Institutions of Decentralization and Power-Sharing J.Vickers Gendered Institutionalist Analysis: Understanding Democratic Transitions G.Waylen Nested Newness and Institutional Innovation: Expanding Gender Justice in the International Criminal Court L.Chappell Conclusion: Towards a Feminist Institutionalism? F.Mackay
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".