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Record W1910666344 · doi:10.5539/gjhs.v8n3p1

Marriage Patterns and Childbearing: Results From a Quantitative Study in North of Iran

2015· article· en· W1910666344 on OpenAlexvenueno aff
Ziba Taghizadeh, Fereshteh Behmanesh, Abbas Ebadi

Bibliographic record

VenueGlobal Journal of Health Science · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsnot available
Fundersnot available
KeywordsFertilityCluster samplingDemographyConvergence (economics)PopulationReproductive behaviorFamily planningCluster (spacecraft)Demographic economicsGeographyResearch methodologyEconomic growthSociologyEconomics

Abstract

fetched live from OpenAlex

<span style="font-family: Times New Roman; font-size: small;"> </span><p>Social changes have rapidly removed arranged marriages and it seems the change in marriage pattern has played a role in childbearing. On the other hand, there is a great reduction in population in many countries which requires a comprehensive policy to manage the considerable drop in population. To achieve this goal, initially, the factors affecting fertility must be precisely identified. This study aims to examine the role of marriage patterns in childbearing. In this cross-sectional quantitative study, 880 married women 15-49 years old, living in the north of Iran were studied using a cluster sampling strategy. The results showed that there are no significant differences in reproductive behaviors of three patterns of marriage in Bobol city of Iran. It seems there is a convergence in childbearing due to the different patterns of marriage and Policymakers should pay attention to other determinants of reproductive behaviors in demographic planning.</p><p> </p><span style="font-family: Times New Roman; font-size: small;"> </span>

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)0.005
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 score0.099
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.157
GPT teacher head0.415
Teacher spread0.257 · 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

Citations26
Published2015
Admission routes1
Has abstractyes

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