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Record W3035382687 · doi:10.1177/1069397120931031

Sex Difference on the Importance of Veiling: A Cross-Cultural Investigation

2020· article· en· W3035382687 on OpenAlexaff
Farid Pazhoohi, Alan Kingstone

Bibliographic record

VenueCross-Cultural Research · 2020
Typearticle
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAttractivenessFidelityArgument (complex analysis)PsychologyDevelopmental psychologySocial psychologyGender studiesSociologyMedicine

Abstract

fetched live from OpenAlex

Veiling is an ancient cultural practice endorsed by religion, social institutions, and laws. Recently, there have been adaptive arguments to explain its function and existence. Specifically, it is argued that veiling women is a form of male mate guarding strategy, which aims to increase sexual fidelity by decreasing overt displays of his mate’s physical attractiveness, thereby helping to secure his reproductive success. Furthermore, it is suggested that such mate retention strategies (veiling) should be more important when child survival is more precarious, as cues to sexual fidelity support higher paternal investment. Using publicly available data from the PEW Research Center encompassing 26,282 individuals from 25 countries, we tested the hypotheses that men should be more supportive of women’s veiling and this support should be more important in harsher environments, particularly those with poor health and high mortality rates, where paternal care is presumably more important. Our results show that men were more supportive of veiling than women, and this support increased as the environments became harsher. Overall, these findings support the male mate retention argument as well as the idea that the practice of veiling is sensitive to environmental differences.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.005
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.307
GPT teacher head0.514
Teacher spread0.207 · 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; both teacher heads agree on what is shown here.

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

Citations19
Published2020
Admission routes1
Has abstractyes

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