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Record W2182595036 · doi:10.1177/147470490900700201

Sex Differences in the Use of Indirect Aggression in Adult Canadians

2009· article· en· W2182595036 on OpenAlexaffabout
Gail Moroschan, Peter L. Hurd, Elena Nicoladis

Bibliographic record

VenueEvolutionary Psychology · 2009
Typearticle
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAggressionPsychologyVerbal aggressionDevelopmental psychologyPopulationInjury preventionPoison controlClinical psychologyDemographyMedicineMedical emergency

Abstract

fetched live from OpenAlex

Evolutionary psychologists have argued that the emergence of language was associated with reducing direct physical aggression and easing social functioning in small groups. If this is so, then males should use verbal or indirect aggression more frequently than females since they engage in more direct aggression. A recent study found no significant differences between men and women's self-reports of indirect aggression in a U.K. sample. We administered the same questionnaire to 175 male and 311 female Canadian university students. Men in this population reported using indirect aggression more frequently than women. The Canadian participants generally reported using indirect aggression less frequently than the U.K. study sample did, particularly the women. These results suggest that there are cultural differences in adults' frequency of use of indirect aggression. We review a number of possible reasons to account for these different results.

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 score0.051
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.078
GPT teacher head0.348
Teacher spread0.270 · 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

Citations18
Published2009
Admission routes2
Has abstractyes

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