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Record W2969680378

A Case Study of Sexual Assault on Post-Secondary Campuses

2019· article· en· W2969680378 on OpenAlexaffabout
Taylor Kylie MacKenzie

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSexual assaultCriminologyQualitative researchPsychologySexual violencePolitical scienceSocial psychologyPublic relationsHuman factors and ergonomicsPoison controlMedicineSociologyMedical emergencySocial science
DOInot available

Abstract

fetched live from OpenAlex

There is an endemic problem of sexual assault being perpetuated on university campuses. It has been argued in previous literature that one in four women are sexually assaulted while in university, making this issue pervasive and of great importance to study. There is a gap in Canadian literature surrounding university responses to sexual assault, and the implementation and effectiveness of universities policies enacted to support survivors of sexual assault. This study will contribute to filling the existing gaps surrounding responses to sexual assault and in relation to sexual assault policies. A qualitative methodological approach of a case study using four cases was employed to explore how universities respond to sexual assault issues, and how policies inform these responses. I compiled articles surrounding the four cases and undertook a case study methodology to explore relevant themes among the articles. The findings from the study suggest that universities respond poorly to sexual assault as there is a lack of, or insufficient policies, which often results in the universities inability to respond to cases adequately, and inability to help the survivors. This research sheds light on the importance of studying sexual assault and policies on university campuses to improve the response and management of cases by universities.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.464
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.046
GPT teacher head0.352
Teacher spread0.306 · 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 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
Published2019
Admission routes2
Has abstractyes

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