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Record W4289278152 · doi:10.21203/rs.3.rs-1885750/v1

Intrasexual Competition may be Greater between Unrelated Women than Men

2022· preprint· en· W4289278152 on OpenAlexafffund
Joyce F. Benenson, Henry Markovits

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsUniversité du Québec à Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCompetition (biology)Resource (disambiguation)Sexual selectionDemographyOperational sex ratioPsychologySex ratioBiologyEcologyPopulationSociology

Abstract

fetched live from OpenAlex

Abstract 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 participants, 25 to 45 years of age with at least one child from three different countries to evaluate how most same-sex individuals 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 can be even more competitive than 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)0.001
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 score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.000

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 head0.160
GPT teacher head0.464
Teacher spread0.304 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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