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Record W2113584032 · doi:10.1371/journal.pone.0055851

Social Exclusion: More Important to Human Females Than Males

2013· article· en· W2113584032 on OpenAlexaff
Joyce F. Benenson, Henry Markovits, Brittney A. Hultgren, Tuyet A. Nguyen, Grace Bullock, Richard W. Wrangham

Bibliographic record

VenuePLoS ONE · 2013
Typearticle
Languageen
FieldPsychology
TopicDeath Anxiety and Social Exclusion
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsSocial exclusionPsychologySocial groupContrast (vision)DemographySocial psychologyBiologyDevelopmental psychologySociology

Abstract

fetched live from OpenAlex

Theoretical models based on primate evidence suggest that social structure determines the costs and benefits of particular aggressive strategies. In humans, males more than females interact in groups of unrelated same-sex peers, and larger group size predicts success in inter-group contests. In marked contrast, human females form isolated one-on-one relationships with fewer instrumental benefits, so social exclusion constitutes a more useful strategy. If this model is accurate, then human social exclusion should be utilized by females more than males and females should be more sensitive to its occurrence. Here we present four studies supporting this model. In Study 1, using a computerized game with fictitious opponents, we demonstrate that females are more willing than males to socially exclude a temporary ally. In Study 2, females report more actual incidents of social exclusion than males do. In Study 3, females perceive cues revealing social exclusion more rapidly than males do. Finally, in Study 4, females' heart rate increases more than males' in response to social exclusion. Together, results indicate that social exclusion is a strategy well-tailored to human females' social structure.

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)0.001
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.090
GPT teacher head0.332
Teacher spread0.242 · 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

Citations194
Published2013
Admission routes1
Has abstractyes

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Same venuePLoS ONESame topicDeath Anxiety and Social ExclusionFrench-language works237,207