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Record W4290780058 · doi:10.21203/rs.3.rs-1748542/v1

Global age disparity in marriage provides support for the role of mate choice in the evolution of maternal mortality and menopause

2022· preprint· en· W4290780058 on OpenAlexafffund
Mindy Pru, Michelle Brown, Rama S. Singh

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsMcMaster University
FundersMcMaster University
KeywordsDemographyMenopauseEthnic groupAge at first marriageMortality rateMedicineFertilityPopulationSociology

Abstract

fetched live from OpenAlex

Abstract Mate choice, marriage, and menopause are life-altering events affecting women’s health. It has been hypothesized that mate choice through age disparity in marriage may have contributed to the evolution of menopause and the persistence of maternal mortality. Thus, the purpose of this study was to explore and document evidence for age disparity in marriage and evaluate its effects on maternal mortality. Data on couple’s age at marriage was collected from various sources and ethnic populations. The results showed that males were significantly older than females at first marriage in all populations analyzed. While age disparate relationships appeared universal, their effects on maternal mortality in present populations were modest (R2 = 0.43) and complex, with a multitude of factors affecting maternal mortality. However, it was observed that Burkina Faso, Guinea, and Nigeria all stood within the top 15 countries with the highest age disparities in marriage, maternal mortality, child marriage, and polygamy rates. The results from this study provided support for the mate choice theory of menopause and maternal mortality and suggested that past rates of child marriage and maternal mortality would have been higher and persistent, affecting women’s health.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.103
GPT teacher head0.443
Teacher spread0.340 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2022
Admission routes2
Has abstractyes

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