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Record W2336887292 · doi:10.1017/s0021932016000109

LOW FERTILITY INTENTION IN TEHRAN, IRAN: THE ROLE OF ATTITUDES, NORMS AND PERCEIVED BEHAVIOURAL CONTROL

2016· article· en· W2336887292 on OpenAlexaff
Amir Erfani

Bibliographic record

VenueJournal of Biosocial Science · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Dynamics and Relationships
Canadian institutionsNipissing University
Fundersnot available
KeywordsNormativeFertilityTheory of planned behaviorPsychologyNormative social influenceSocial psychologyMultivariate analysisControl (management)Birth controlFamily planningDeveloping countryPopulationDemographyDevelopmental psychologyMedicineEconomicsSociologyPolitical scienceResearch methodologyEconomic growth

Abstract

fetched live from OpenAlex

Persistent low fertility rates are an increasing concern for countries with low fertility like Iran. Informed by the Theory of Planned Behaviour, this study examined the immediate factors influencing fertility intentions, using data from the 2012 Tehran Survey of Fertility Intentions. The findings show that more than half of young married adults in Tehran intend to have no more children. The multivariate analysis results indicate that individuals who view childbearing as being detrimental to their personal life, feel less normative pressure to have a/another child, and believe their childbearing decision is not contingent on the presence of economic resources required for childbearing, are more likely to want no (more) children or to be unsure rather than to want a/another child. Attitudes and normative pressure are dominant factors influencing the intention to have a first child, while the intention to have a second child is mainly affected by attitudes and perceived constraints. The policy implications of the results are discussed.

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 score0.030
Threshold uncertainty score0.060

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.289
Teacher spread0.268 · 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

Citations34
Published2016
Admission routes1
Has abstractyes

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