Can the health effects of widely-held societal norms be evaluated? An analysis of the United Nations convention on the elimination of all forms of discrimination against women (UN-CEDAW)
Bibliographic record
Abstract
BACKGROUND: Female life expectancy and mortality rates have been improving over the course of many decades. Many global changes offer potential explanations. In this paper, we examined whether the United Nations Convention on the Elimination of All Forms of Discrimination Against Women (CEDAW) has, in part, been responsible for the observed improvements in these key population metrics of women's health. METHODS: Data were obtained from the United Nations Treaty Series Database, the World Bank World Development Indicators database and, the Polity IV database. Because CEDAW is nearly universally ratified, it was not feasible to compare ratifying countries to non-ratifying countries. We therefore applied interrupted times series analyses, which creates a comparator (counterfactual) scenario by using the trend in the health outcome before the policy exposure to mathematically determine what the trend in the health outcome would have been after the policy exposure, had the policy exposure not occurred. Analyses were stratified by country-level income and democratization. RESULTS: Among low-income countries, CEDAW improved outcomes in democratic, but not non-democratic countries. In middle-income countries, CEDAW largely had no effect and, among high-income countries, had largely positive effects. CONCLUSIONS: While population indicators of women's health have improved since CEDAW ratification, the impact of CEDAW ratification itself on these improvements varies across countries with differing levels of income and democratization.
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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 | 0.048 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".