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Record W115336985 · doi:10.25336/p6hw2c

Fertility in Islamic Republic of Iran: Levels, Trends and Differentials during Three Decades (1967-1996)

2002· article· en· W115336985 on OpenAlexvenueno aff
Kazem Mohammad, Farideh Khalaj Abadi Farahani, Mehdi Rahgozar, Mahmood Mahmoodi Majdabadi Farahani

Bibliographic record

VenueCanadian Studies in Population · 2002
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
FundersMinistry of Health and Medical EducationUnited Nations Population Fund
KeywordsFertilityDemographyTotal fertility rateBaby boomPopulationBirth rateBirth controlGeographyFamily planningSociologyResearch methodology

Abstract

fetched live from OpenAlex

This study focuses on the fertility levels and trends of Iranian women aged 10- 49 years during three decades, 1967-1996. Information was collected by interviewing a systematic cluster sample of 13,253 households. The basic date of concern was the date of each live born child for a sample of women in reproductive age and whether the live born was alive at the time of the study. The detailed nature of these data collected for each birth enabled fertility rates to be calculated for exact periods before the survey, for exact calendar years. Higher fertility rates were found for the period 10-15 and 15-20 years preceding the survey. These high fertility levels are probably due to some socio-economic changes in favor of higher level of fertility immediately after the 1979 revolution. However the results of the present survey show during 1967-1996, the TFR1 had dropped from 6.38 to 2.88 (an expected TFR based on fertility behavior during 1992-96). This decline was probably due to delay in marriage and policy changes in favor of population control particularly since 1987. Despite this potential fertility decline in recent years, Iran is expected to face a baby boom as the offspring who were born during the 1976-86 reach to the reproductive age in the coming decades.

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)0.000
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 score0.656
Threshold uncertainty score0.912

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.095
GPT teacher head0.330
Teacher spread0.235 · 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

Citations3
Published2002
Admission routes1
Has abstractyes

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