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Record W2149422285 · doi:10.1098/rsbl.2007.0410

Voice pitch predicts reproductive success in male hunter-gatherers

2007· article· en· W2149422285 on OpenAlexaff
Coren L. Apicella, David R. Feinberg, Frank W. Marlowe

Bibliographic record

VenueBiology Letters · 2007
Typearticle
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsMcMaster University
FundersHarvard UniversityNational Science Foundation
KeywordsBiologySexual dimorphismReproductive successSexual selectionProxy (statistics)DemographyPopulationSelection (genetic algorithm)EcologyZoology

Abstract

fetched live from OpenAlex

The validity of evolutionary explanations of vocal sexual dimorphism hinges upon whether or not individuals with more sexually dimorphic voices have higher reproductive success than individuals with less dimorphic voices. However, due to modern birth control methods, these data are rarely described, and mating success is often used as a second-rate proxy. Here, we test whether voice pitch predicts reproductive success, number of children born and child mortality in an evolutionarily relevant population of hunter-gatherers. While we find that voice pitch is not related to reproductive outcomes in women, we find that men with low voice pitch have higher reproductive success and more children born to them. However, voice pitch in men does not predict child mortality. These findings suggest that the association between voice pitch and reproductive success in men is mediated by differential access to fecund women. Furthermore, they show that there is currently selection pressure for low-pitch voices in men.

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)0.000
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.340
Teacher spread0.312 · 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

Citations257
Published2007
Admission routes1
Has abstractyes

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