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Record W2789817112 · doi:10.20381/ruor-6723

The Role of Gender-Related Constructs in the Tolerance of Dating Violence: A Multivariate Analysis

2014· dissertation· en· W2789817112 on OpenAlexaboutno aff
Sarah A. MacLean

Bibliographic record

VenueuO Research (University of Ottawa) · 2014
Typedissertation
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDating violenceMultivariate statisticsMultivariate analysisPsychologyStatisticsMedicineMathematicsHuman factors and ergonomicsEnvironmental healthPoison controlDomestic violence

Abstract

fetched live from OpenAlex

Using a purposive sampling technique, this study employed an online questionnaire to assess the relationship between attitudes towards gender-related constructs (e.g. rape myth acceptance, shared power in relationships, the acceptability of dating violence and perceived seriousness of dating violence) and the tolerance of dating violence among undergraduate students in the Faculty of Social Science at the University of Ottawa. Linear regression models were conducted to identify the most salient predictors of the tolerance of dating violence. A general/combined model was examined as well as three subtype-specific models (e.g. psychological, physical and sexual dating violence). A total of seven predictor variables were entered into each model in three blocks: sociodemographic variables were entered first, followed by sex and then gender-related constructs (e.g. rape myth acceptance, power in relationships, the acceptability and seriousness of dating violence). The results identify a number of variables that are associated with the tolerance of dating violence scales and some that led to a decrease in scores on these scales. Findings suggest that the link between gender-related constructs and the tolerance of dating violence is complex and multidimensional and warrants further research to explain the variation observed.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.046
GPT teacher head0.371
Teacher spread0.324 · 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.

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

Citations0
Published2014
Admission routes1
Has abstractyes

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