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Record W4251325019 · doi:10.24124/2018/58867

"Locker room talk": the impact of men degrading women to other men

2018· dissertation· en· W4251325019 on OpenAlexaff
Rebecca L. Collins

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsConversationDelegatePsychologySocial psychologyDevelopmental psychologyCommunication

Abstract

fetched live from OpenAlex

The present research examines the effects of “locker room talk” by exploring whether conversations among men about sexual activity with women give rise to sexist and rapepromoting attitudes. Male participants listened to an audio recording of either degrading or respectful “locker room talk”. Those exposed to the degrading conversation were expected to exhibit more negative attitudes toward women and stronger rape condoning attitudes. In addition, sexist males were expected to delegate masculine status to men who degraded women. Hypothesis one was not supported, but hypothesis two received partial support: Relative to men lower in sexism, males with higher sexism scores allocated masculine status to men who talked about their sexual encounters regardless of the conversation’s degrading or respectful nature. Men scoring higher in sexism were more likely to endorse rapesupportive attitudes, to hold negative attitudes towards women, and to objectify the woman being discussed in the conversation.

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)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.031
GPT teacher head0.384
Teacher spread0.352 · 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 designQualitative
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
Published2018
Admission routes1
Has abstractyes

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