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Record W2950137276

Women’s Experiences of Stalking on Campus: Behaviour Changes and Access to University Resources

2018· article· en· W2950137276 on OpenAlexaffvenue
Lana Stermac, Jenna Cripps, Veronica Badali

Bibliographic record

VenueCanadian women's studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicStalking, Cyberstalking, and Harassment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsStalkingThematic analysisPsychologyUniversity campusSAFERHarassmentSocial psychologyApplied psychologyQualitative researchSociologyCriminologyEngineering
DOInot available

Abstract

fetched live from OpenAlex

The present study examined whether behavioural changes made by women students who experienced stalking on campus restricted their access to university resources and facilities. A diverse sample of two hundred and fifty-five university women with experiences of stalking, including racialized women, women with disabilities and members of sexual minorities, completed an online survey and asked if they had made changes to their behaviour on campus in order to feel safer or more secure and to describe those changes. Using phenomenological thematic analysis and a feminist theory framework of understanding stalking behaviour, themes and subthemes relevant to the research questions were generated. Thematic analysis of participant’s responses yielded five themes based on changes in behaviours or attitudes identified by students: restrictions in campus movements, increased vigilance, changes in social engagement, changes in academic engagement, and use of health/support services. Women self-identifying with a disability were more likely to report changing their behaviour on campus to feel safer and more comfortable following stalking victimization compared to individuals without disabilities.

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)0.004
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 score0.956
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.306
Teacher spread0.267 · 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

Citations2
Published2018
Admission routes2
Has abstractyes

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