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Record W2140259566 · doi:10.1017/s0021932010000556

INTERGENERATIONAL TRANSMISSION OF REPRODUCTIVE BEHAVIOUR IN BOTSWANA

2010· article· en· W2140259566 on OpenAlexaff
Tabitha T. Langeni

Bibliographic record

VenueJournal of Biosocial Science · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsResidenceDemographyMarital statusFertilityStratified samplingCohortPopulationPsychologyMedicineSociology

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate intergenerational transmission of reproductive behaviour in Botswana. The major source of data was the 2001 Botswana AIDS Impact Survey where a nationally representative random sample of men and women aged 10-64 years was selected using a stratified two-stage probability sample design. Covariates in the analysis include age, education, marital status, religion, age at first birth, residence, duration at residence and contraceptive use. The main analytical technique is linear regression. The results indicate that the reproductive behaviour of older generations has a significantly positive influence on the reproductive behaviour of the subsequent generation, but does not affect the subsequent generation homogeneously. The effect appeared much stronger for women who initiated childbearing at an older age, for women who had never been to school, and for the cohort aged 50-59 years. These findings suggest that number of siblings, as a reproductive behaviour determinant, may very well have confounded previous reproductive behaviour analyses in Botswana. The study draws attention to the importance of the effect of origin family size in determining reproductive behaviour outcomes in Botswana.

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 score0.035
Threshold uncertainty score0.069

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.022
GPT teacher head0.338
Teacher spread0.316 · 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

Citations7
Published2010
Admission routes1
Has abstractyes

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