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Record W4220984174 · doi:10.1215/00703370-9815547

The Rise of Sonless Families in Asia and North Africa

2022· article· en· W4220984174 on OpenAlexaboutno aff
Roshan K. Pandian, Keera Allendorf

Bibliographic record

VenueDemography · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsnot available
Fundersnot available
KeywordsFertilityDemographyChinaEast AsiaGeographyQuarter (Canadian coin)PopulationTotal fertility ratePopulation growthFamily planningSocioeconomicsSociologyResearch methodology

Abstract

fetched live from OpenAlex

A neglected consequence of declining fertility is the likely rise of families with children of one sex-only sons or only daughters. Increases in such families present important demographic shifts that may weaken patrilineal family systems. We assess whether sons-only and daughters-only families rose in Asia and North Africa from the early 1990s to around 2015. Using 88 surveys and two censuses, we examine how the number and sex composition of children of mothers aged 40-49 changed across 20 countries, representing 87% of the region's population and 54% of the global population. We also compare observed trends to sex-indifferent counterfactuals, quantify contributions of fertility declines with decompositions, and investigate subnational trends in China and India. Increases in sons-only families were universal where numbers of children fell. Growth of daughters-only families was suppressed in patrilineal contexts, but these sonless families still rose significantly in 13 of 18 countries where numbers declined. By 2015, over a quarter of families in the region had only sons and nearly a fifth only daughters. There was considerable variation across countries: recent levels ranged from 28.3% to 3.4% daughters-only and from 40.1% to 6.0% sons-only. China and the rest of East Asia had the highest shares.

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)0.001
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 score0.254
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
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 head0.033
GPT teacher head0.257
Teacher spread0.224 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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