Paper prepared for delivery at the American Political Science Association Annual Meeting
Bibliographic record
Abstract
In Pakistan, a woman who reports rape can expect to be charged with adultery; in neighboring India, women-run police stations and new legal devices empower victims of violence and help state officials prosecute sex crimes. In Catholic Ireland, abortion is a crime while in Italy, seat of the Vatican, access to abortion is not only legally guaranteed but also provided at state expense. In Canada and the United Kingdom, women enjoy up to three years of paid maternity leave; in the United States, by contrast, they are not entitled to paid leave at all. These gender-related public policies vary dramatically across societies. They shape women’s access to education and employment, their ability to care for their children and other family members, and their chances to escape poverty and enjoy good health. Gender-related policies have broader implications as well: societies with greater gender equality are more likely to be prosperous and sustain stable democratic institutions. And children have better chances of surviving and leading healthy lives in more gender-equal societies (Sen 1999; Dreze and Sen 2002; Nussbaum 2001; Inglehart and Norris 2003). What factors push governments to advance or undermine women’s rights and
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.746 | 0.449 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".