MétaCan
Menu
Back to cohort
Record W2972518037 · doi:10.7202/1063781ar

Where is it? Examining Post-Secondary Students' Accessibility to Policies and Resources on Sexual Violence

2019· article· en· W2972518037 on OpenAlexaffvenueabout
Jacey Magnussen, Irene Shankar

Bibliographic record

VenueCanadian Journal of Higher Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsMount Royal University
Fundersnot available
KeywordsLegislationSexual violenceWork (physics)Sexual assaultPublic relationsPolitical sciencePoison controlSuicide preventionPublic administrationSociologyCriminologyMedicineLawEnvironmental healthEngineering

Abstract

fetched live from OpenAlex

Faced with a growing demand for adequate policies and programs that meaningfully address sexual violence on campus, the provinces of British Columbia, Ontario and Manitoba have introduced legislation requiring all post-secondary institutions to institute a sexual assault policy. The remaining provinces and territories do not have similar legislation. In absence of such legislation, using the case study of Alberta, we examined how equipped post-secondary institutions in this province are to assist students in need. Utilizing publicly available data we examined: 1) whether Alberta’s post-secondary institutions have a sexual violence policy which is readily and easily accessible to the student; and 2) the ease with which students can access university resources and support services for sexual violence. The results indicate that most institutions do not have an accessible policy and support services for students in need. We are hopeful that this study can inform those designing and advocating for sexual violence policies on campus to institute measures to clarify institutions’ sexual violence policies, increase accessibility to those policies, create policies where they are missing, and work on clarifying the availability of resources for students on and off campus.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
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 head0.037
GPT teacher head0.378
Teacher spread0.341 · 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

Citations4
Published2019
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Higher EducationSame topicSexual Assault and Victimization StudiesFrench-language works237,207