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Record W2887675999 · doi:10.7939/r3r78634w

Surveying Intimate Partner Violence Myths Among Post-secondary Students

2017· article· en· W2887675999 on OpenAlexaboutno aff
Nadia Keyes

Bibliographic record

VenueUniversity of Alberta Library · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsnot available
Fundersnot available
KeywordsMythologyDomestic violencePsychologySuicide preventionPoison controlSocial psychologyCriminologyHistoryMedicineMedical emergency

Abstract

fetched live from OpenAlex

The purpose of this study is to extend the rape myth literature to intimate partner violence (IPV) myths by evaluating the prevalence of IPV myth acceptance and clarifying whether gender and prior IPV victimization are associated with IPV myth acceptance. To this end, three research questions were explored: 1) What is the prevalence of IPV myth acceptance amongst a student population? 2) Does gender correlate with IPV myth acceptance? 3) Do victims and non-victims of IPV accept IPV myths differently? University of Alberta students were contacted via posters and classroom presentations to participate in a 15-minute online survey containing a demographic survey, the Marlow-Crown Social Desirability Scale (MC-C; Reynolds, 1982), the Domestic Violence Myth Acceptance scale (DVMAS; Peters, 2003), and three subscales from the Revised Conflict Tactics Scale (CTS2; Straus et al., 1996). Depending upon the criteria used to define acceptance, between 65% (neither agreeing nor disagreeing that “domestic violence rarely happens in my neighbourhood”) and 11% (strongly agreeing that “if a woman doesn’t like it she can leave”) of participants accepted at least one IPV myth. Consistent with expectations, men accepted IPV myths to a greater extent than women, and victims of IPV did not differ from non-victims in their acceptance of IPV myths.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.363
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.016
GPT teacher head0.271
Teacher spread0.255 · 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 teacher head, not a consensus.

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
Published2017
Admission routes1
Has abstractyes

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