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Record W1967436816 · doi:10.1177/0020715211426177

Gender equality, development, and cross-national sex gaps in life expectancy

2011· article· en· W1967436816 on OpenAlexvenueno aff
Carla Medalia, Virginia W. Chang

Bibliographic record

VenueInternational Journal of Comparative Sociology · 2011
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsnot available
Fundersnot available
KeywordsLife expectancyGender equalityConvergence (economics)DemographyGender gapDivergence (linguistics)Demographic economicsPsychologySociologyEconomicsGender studiesEconomic growthPopulation

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.378
GPT teacher head0.568
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2011
Admission routes1
Has abstractyes

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