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Record W4252924338 · doi:10.3138/jcfs.36.2.205

Family Size Control by Infanticide in the Great Agrarian Societies of Asia

2005· article· en· W4252924338 on OpenAlexvenueno aff
John C. Caldwell, Bruce Caldwell

Bibliographic record

VenueJournal of Comparative Family Studies · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsnot available
Fundersnot available
KeywordsDowryPossession (linguistics)Inheritance (genetic algorithm)ChinaAgrarian societyPopulationSubsistence agricultureBirth controlExtended familyPopulation controlGeographyDevelopment economicsIncentivePopulation growthSocioeconomicsSociologyEconomic growthAgricultureFamily planningEconomicsPolitical scienceGenealogyHistoryLawDemographyBiology

Abstract

fetched live from OpenAlex

There is now sufficient historical demographic analysis of India, Japan and China to show that infanticide has traditionally been employed to shape families. The driving force has often been the need to preserve family property from division by inheritance or dispersal by the payment of high dowries, although, especially in China, current subsistence problems have also been important. The practice has often been more frequent among the rich than the poor and may have increased as economic advancement became more possible. Nevertheless, it is probable that high child mortality ensured that most families did not have to practise infanticide, which probably played only a modest role in determining the level of population equilibrium. The factor making infanticide more likely than in hunter-gatherer societies is the possession of land. The Judaeo-Christian-Muslim outlawing of infanticide provided the incentive both to try to eliminate the practice and to record its existence.$$infanticide, historical demography, population control, agricultural societies, land, inheritance, dowry, India, Japan, China.

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.404
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.101
GPT teacher head0.382
Teacher spread0.281 · 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 designQualitative
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

Citations28
Published2005
Admission routes1
Has abstractyes

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