Engendering Justice: Constructing Institutions to Address Violence Against Women
Bibliographic record
Abstract
This paper addresses how states improve their responsiveness to violence against women in developing countries with little political will and few resources to do so. One key to engendering justice and improving responsiveness is building specialized institutions within the state that facilitate the implementation of laws addressing violence against women. Why and how do states engage in institution-building to protect marginalized populations in these contexts? I propose that developing countries are more likely to create and maintain specialized institutions when domestic and international political and legal frameworks make the state more vulnerable to women’s demands, and when civil society coordinates with the state and/or international organizations to take advantage of this political opportunity. This coordination brings necessary pressure and resources that would be difficult, if not impossible, to deliver otherwise. This inter-institutional coordination is necessary for building and maintaining new state institutions and programs that help to monitor the implementation of laws, develop public policies, provide services for victims, and improve responsiveness of the justice system. This fills an important lacuna in the literature, which focuses on women’s state institutions as an important catalyst for responsiveness to violence against women, but does not explain how these institutions are initially constructed.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".