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Record W1562851158 · doi:10.3386/w20474

The Biocultural Origins of Human Capital Formation

2014· preprint· en· W1562851158 on OpenAlexaboutno aff
Oded Galor, Marc Klemp

Bibliographic record

VenueNational Bureau of Economic Research · 2014
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsHuman capitalSociologyAnthropologyEconomic geographyGeographyEconomicsEconomic growth

Abstract

fetched live from OpenAlex

This research explores the biocultural origins of human capital formation. It presents the first evidence that moderate fecundity and thus predisposition towards investment in child quality was conducive for long-run reproductive success within the human species. Using an extensive genealogical record for nearly half a million individuals in Quebec from the sixteenth to the eighteenth centuries, the study explores the effect of fecundity on the number of descendants of early inhabitants in the subsequent four generations. The research exploits variation in the random component of the time interval between the date of first marriage and the first birth to establish that while higher fecundity is associated with a larger number of children, an intermediate level maximizes long-run reproductive success. Moreover, the observed hump-shaped effect of fecundity on long-run reproductive success reflects the negative effect of higher fecundity on the quality of each child. The finding further indicates that the optimal level of fecundity was below the population median, lending credence to the hypothesis that during the Malthusian epoch, the forces of natural selection favored individuals with lower fecundity and thus larger predisposition towards child quality, contributing to human capital formation, the onset of the demographic transition and the evolution of societies from an epoch of stagnation to sustained economic growth.

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)0.003
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.457
Threshold uncertainty score0.777

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 science0.0010.000
Research integrity0.0000.001
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 head0.295
GPT teacher head0.437
Teacher spread0.142 · 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 designTheoretical or conceptual
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

Citations20
Published2014
Admission routes1
Has abstractyes

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