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Migration Experience and Premarital Sexual Initiation in Urban Kenya: An Event History Analysis

2012· article· en· W1998406381 on OpenAlexfundno aff
Nancy Luke, Hongwei Xu, Blessing Mberu, Rachel Goldberg

Bibliographic record

VenueStudies in Family Planning · 2012
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent Sexual and Reproductive Health
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentAfrican Population and Health Research CenterMcGill UniversityBrown University
KeywordsResidenceAffect (linguistics)Premarital sexDemographyPsychologyPopulationHuman sexualityDevelopmental psychologyGender studiesSociology

Abstract

fetched live from OpenAlex

Migration during the formative adolescent years can affect important life-course transitions, including the initiation of sexual activity. In this study, we use life history calendar data to investigate the relationship between changes in residence and timing of premarital sexual debut among young people in urban Kenya. By age 18, 64 percent of respondents had initiated premarital sex, and 45 percent had moved at least once between the ages of 12 and 18. Results of the event history analysis show that girls and boys who move during early adolescence experience the earliest onset of sexual activity. For adolescent girls, however, other dimensions of migration provide protective effects, with greater numbers of residential changes and residential changes in the last one to three months associated with later sexual initiation. To support young people's ability to navigate the social, economic, and sexual environments that accompany residential change, researchers and policymakers should consider how various dimensions of migration affect sexual activity.

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 score0.020
Threshold uncertainty score0.039

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 studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.329
GPT teacher head0.501
Teacher spread0.171 · 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

Citations42
Published2012
Admission routes1
Has abstractyes

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