Bibliographic record
Abstract
This paper develops a framework for explaining why the implementation of culture versus women’s rights policies vary by drawing upon claims from the comparative politics, public policy, and political theory literature. I begin by explaining positive outcomes. I argue that the types of justice multicultural compromises deliver, to what extent, for whom, and why, vary according to political will and state capacity. I hypothesize that the strength of social movements, the openness of the public sphere, and the position of influential leaders in civil society shape the political will of policymakers to pressure the bureaucracy to implement the policy. Bureaucratic quality and performance, judicial independence, and the strength of institutions in the state are also crucial, as they constitute the state’s capacity to enact the policy. I also discuss negative outcomes. I hypothesize that when intense collective action is necessary for passage a decline in collective action will occur. When the policy targets a specific group, state regulatory power and group reification will increase. When policymakers ignore inequalities within the group, I hypothesize that implementation of the policy will increase group inequality. If these outcomes are pervasive, I expect increased intergroup tensions and violence against women. The paper concludes with some preliminary findings from Canada, India, and South Africa that investigate these hypotheses.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".