MétaCan
Menu
Back to cohort
Record W4327813382 · doi:10.1038/s41598-023-31816-0

Married women with children experience greater intrasexual competition than their male counterparts

2023· article· en· W4327813382 on OpenAlexafffund
Joyce F. Benenson, Henry Markovits

Bibliographic record

VenueScientific Reports · 2023
Typearticle
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsUniversité du Québec à Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCompetition (biology)DemographySexual selectionResource (disambiguation)PsychologyDevelopmental psychologyBiologyEcologySociology

Abstract

fetched live from OpenAlex

Human males are considered to be more competitive than females. However, females must also compete for resources necessary for their own and their offsprings' survival. Since females use more indirect forms of competition than males, comparing observable forms of competition may be misleading. One critical driver of competition is resource asymmetry. Since competition occurs primarily within sex, reactions to resource asymmetry with same-sex peers should provide an important measure of competitiveness. We asked 596 married participants, 25-45 years of age with at least one child from three different countries to evaluate how same-sex individuals they know would react to a target individual who had a valuable resource that the same-sex individuals did not have. Half the participants evaluated reactions to same-sex targets, while the other half evaluated reactions to other-sex targets. Participants reported that women would react more negatively than men to resource asymmetry with same-sex targets, but not other-sex targets. These results suggest that women may be even more competitive than men in contexts when important resources related to reproductive success are at stake.

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 categoriesInsufficient 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 score0.075
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.001

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 head0.027
GPT teacher head0.298
Teacher spread0.271 · 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

Citations6
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueScientific ReportsSame topicEvolutionary Psychology and Human BehaviorFrench-language works237,207