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Record W2142926638 · doi:10.1177/0002716213480792

Dimensions of Rural-to-Urban Migration and Premarital Pregnancy in Kenya

2013· article· en· W2142926638 on OpenAlexfundno aff
Hongwei Xu, Blessing Mberu, Rachel Goldberg, Nancy Luke

Bibliographic record

VenueThe Annals of the American Academy of Political and Social Science · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentAfrican Population and Health Research CenterMcGill UniversityBrown University
KeywordsPremarital sexPregnancyReproductive healthDeveloping countryRural areaAffect (linguistics)DemographyMedicineEnvironmental healthGeographySocioeconomicsPsychologyPopulationEconomic growthSociologyEconomics

Abstract

fetched live from OpenAlex

Rural-to urban migration is increasingly common among youth and could affect sexual activities. We use life history calendar data collected in Kisumu, Kenya, to investigate how the timing and number of rural-to-urban moves are associated with premarital pregnancy. Among sexually experienced young women aged 18-24 (N=226), 39 percent have experienced a premarital pregnancy and 60 percent experienced a move in the last 10 years. Results of the event history analysis show that those who experienced one or two moves or whose most recent move occurred in the last seven to 12 months are at increased risk of premarital pregnancy compared to nonmovers. Those whose last move occurred at age 13 or younger were also at an elevated risk. Migration brings about specific needs for youth, including the need for sexual and reproductive health education and services, which should be made available and accessible to new urban residents.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.035
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.038
GPT teacher head0.361
Teacher spread0.323 · 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

Citations12
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueThe Annals of the American Academy of Political and Social ScienceSame topicMigration and Labor DynamicsFrench-language works237,207