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Record W7120510069

CampUS Safety Project: A Model for Engaging Young People to Prevent Violence Against Women on Post-Secondary Campuses in Canada

2016· article· en· W7120510069 on OpenAlexaboutno aff

Bibliographic record

VenuePortuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsnot available
Fundersnot available
KeywordsInterimSuicide preventionPoison controlOccupational safety and healthInstitutionHuman factors and ergonomics
DOInot available

Abstract

fetched live from OpenAlex

Colleges and universities are home to young women whom are at the highest statistical risk of experiencing gender-based violence³. In this, it makes sense that campus administration, service providers and campus police come together to consider ways to address violence against women on campuses. Postsecondary stakeholders should ask: What safety concerns are young women facing? What help is available to support young women on campus? What measures is our post-secondary institution undertaking to prevent and reduce violence against young women on campus? This paper focuses on a Canadian-based initiative on engaging young people to prevent violence against women on post-secondary campuses. The CampUS Safety Project was initiated by Interim Place, a community organization providing shelter and support to abused women, and the University of Toronto Mississauga. The goals of CampUS were to conduct a campus safety audit; research the experiences of young women on campus; develop a community campus safety plan; implement an education, awareness and violence prevention campaign; and share best practices with other campuses. In this paper, I share highlights of what was learned throughout the CampUS project. Highlights include recommendations applicable to preventing and reducing violence against young women on post-secondary campuses elsewhere.

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)0.006
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.688
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0020.001
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 head0.050
GPT teacher head0.376
Teacher spread0.326 · 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 designOther design
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
Published2016
Admission routes1
Has abstractyes

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