MétaCan
Menu
Back to cohort

Moderation of Female–Female Competition for Matings by Competitors’ Age and Parity

2017· book· en· W2730338379 on OpenAlexaff
Melanie MacEacheron, Lorne Campbell

Bibliographic record

VenueOxford University Press eBooks · 2017
Typebook
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsWestern University
Fundersnot available
KeywordsModerationSexual selectionCompetition (biology)Competitor analysisPsychologyReproductive successDevelopmental psychologyDemographySocial psychologyBiologyEcologySociologyPopulationEconomics

Abstract

fetched live from OpenAlex

Previous research on female intrasexual competition, especially but not only for matings or mateships, has largely been conducted using convenience samples of women of undergraduate status and therefore generally between the ages of 17 and 22. Even among such articles including women over 25, the majority do not focus on mate competition. There is a priori reason, however, to believe that intrasexual competition for matings and mateships would extend and change beyond this life stage. This chapter provides an overview of the literature on female intrasexual competition over women’s reproductive careers, discusses factors that should result in predictable changes in female intrasexual competition as women age, and proposes testable hypotheses that should help guide future research in this area of research. Based on this analysis, new theories concerning reproductive advantage derivable from acquiring the status of successful mother are proposed.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.048
GPT teacher head0.289
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

Citations1
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueOxford University Press eBooksSame topicEvolutionary Psychology and Human BehaviorFrench-language works237,207