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Record W2306841286 · doi:10.1098/rstb.2015.0153

Wealth, fertility and adaptive behaviour in industrial populations

2016· review· en· W2306841286 on OpenAlexafffund
Gert Stulp, Louise Barrett

Bibliographic record

VenuePhilosophical Transactions of the Royal Society B Biological Sciences · 2016
Typereview
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsUniversity of Lethbridge
FundersNatural Sciences and Engineering Research Council of CanadaNederlandse Organisatie voor Wetenschappelijk OnderzoekCanada Research Chairs
KeywordsFertilityPerspective (graphical)EconomicsPopulationSociologyDemography

Abstract

fetched live from OpenAlex

The lack of association between wealth and fertility in contemporary industrialized populations has often been used to question the value of an evolutionary perspective on human behaviour. Here, we first present the history of this debate, and the evolutionary explanations for why wealth and fertility (the number of children) are decoupled in modern industrial settings. We suggest that the nature of the relationship between wealth and fertility remains an open question because of the multi-faceted nature of wealth, and because existing cross-sectional studies are ambiguous with respect to how material wealth and fertility are linked. A literature review of longitudinal studies on wealth and fertility shows that the majority of these report positive effects of wealth, although levels of fertility seem to fall below those that would maximize fitness. We emphasize that reproductive decision-making reflects a complex interplay between individual and societal factors that resists simple evolutionary interpretation, and highlight the role of economic insecurity in fertility decisions. We conclude by discussing whether the wealth-fertility relationship can inform us about the adaptiveness of modern fertility behaviour, and argue against simplistic claims regarding maladaptive behaviour in humans.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.852
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.331
GPT teacher head0.424
Teacher spread0.093 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations62
Published2016
Admission routes2
Has abstractyes

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