Gender equality, development, and cross-national sex gaps in life expectancy
Bibliographic record
Abstract
Female life expectancy exceeds male life expectancy in almost every country throughout the world. Nevertheless, cross-national variation in the sex gap suggests that social factors, such as gender equality, may directly affect or mediate an underlying biological component. In this article, we examine the association between gender equality and the sex gap in mortality. Previous research has not addressed this question from an international perspective with countries at different levels of development. We examine 131 countries using a broad measure of national gender equality that is applicable in both Less Developed Countries (LDCs) and Highly Developed Countries (HDCs). We find that the influence of gender equality is conditional on level of development. While gender equality is associated with divergence between female and male life expectancies in LDCs, it is associated with convergence in HDCs. The relationship between gender equality and the sex gap in mortality in HDCs strongly relates to, but is not explained by, sex differences in lung cancer mortality. Finally, we find that divergence in LDCs is primarily driven by a strong positive association between gender equality and female life expectancy. In HDCs, convergence is potentially related to a weak negative association between gender equality and female life expectancy, though findings are not statistically significant.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".