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Record W4229522112 · doi:10.5539/ijsp.v8n6p88

Sex Preference, Religion and Ethnicity Roles in Fertility Among Women of Childbearing Age in Nigeria: Examining the Links Using Zero-Inflated Poisson Regression Model

2019· article· en· W4229522112 on OpenAlexvenueno aff
Adebowale Ayo Stephen, Asa Soladoye

Bibliographic record

VenueInternational Journal of Statistics and Probability · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsnot available
Fundersnot available
KeywordsHausaFertilityYorubaDemographyTotal fertility rateEthnic groupPoisson regressionPopulationSociologyFamily planningAnthropology

Abstract

fetched live from OpenAlex

The study aimed at examining the independent and joint influence of three cultural factors; religion, sex preference (SP) and ethnicity on fertility in Nigeria. Cross-sectional population-based cluster design approach was used for the study. The investigated population group was women of reproductive age (n=19,348). Probability of bearing ≥5 children, refined Total Fertility Rate and mean fertility were used to assess fertility. Data were analyzed using demographic and Zero-Inflated Poisson models. Fertility indices were higher among the Hausa/Fulani ethnic group than Igbo and Yoruba and also among Muslim women than Christians. Interaction shows that the probability of bearing at least five children was highest among women who; have no SP, belong to Islamic religious denomination, and of Hausa/Fulani ethnic group. The fertility incident rate ratio (IRR) was higher among women with no SP than women who have SP and also higher among Hausa/Fulani than Yoruba but lower among Christians than Muslims. Fertility differentials persists by ethnicity, religion and SP after controlling for other important variables. Difference exists in fertility among religious, ethnic groups and by SP in Nigeria. Fertility reduction strategies should be intensified in Nigeria, but more attention should be given to Muslims and Hausa/Fulani women.

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)0.002
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 score0.127
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.050
GPT teacher head0.313
Teacher spread0.263 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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