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Record W1984216158 · doi:10.1080/00324720215929

Distinctive features of age-specific fertility profiles in the English-speaking world: Common patterns in Australia, Canada, New Zealand and the United States, 1970-98

2002· article· en· W1984216158 on OpenAlexaboutno aff
Tarani Chandola, D. A. Coleman, R. W. Hiorns

Bibliographic record

VenuePopulation Studies · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicInsurance, Mortality, Demography, Risk Management
Canadian institutionsnot available
Fundersnot available
KeywordsFertilityDemographyEthnic groupGeographyIrishTotal fertility ratePolitical scienceSociologyPopulationFamily planningResearch methodologyLinguisticsLaw

Abstract

fetched live from OpenAlex

The paper seeks to identify common features in the fertility patterns of the English-speaking world and provide a model basis for comparison of fertility between countries and over time. Attention is focused on the heterogeneity within the fertility patterns of Australia, Canada, New Zealand, and the United States, similar to that reported earlier for the UK and the Irish Republic. The recent age-specific fertility patterns of these countries display a marked 'bulge' in fertility of women under age 25. A mixture model with two-component Hadwiger functions provides a suitable fit. The heterogeneity thus suggested is related to the proportion of births outside marriage. Additionally, there is some evidence that, in the United States, and lesser extent in New Zealand, this heterogeneity in fertility patterns may be explained by ethnic difference in the timing and number of births.

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)0.002
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 score0.081
Threshold uncertainty score0.162

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.066
GPT teacher head0.326
Teacher spread0.260 · 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

Citations50
Published2002
Admission routes1
Has abstractyes

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