Strengthening Our Response to Sexual Violence: A Working Paper on Prevention and Response Strategies for Selkirk College
Bibliographic record
Abstract
Bill 23: Sexual Violence and Misconduct Act requires that all post-secondary institutions\nestablish and implement a sexual misconduct policy including prevention and response\nmeasures. This paper supports the work that is being done at Selkirk College to address the\nprevention and response requirements of Bill 23, and support a decrease in the number of sexual\nassaults on campus while simultaneously working to increase the number of students who seek\nsupport following a sexual assault. This paper is informed by two Selkirk College institutional\nresearch projects, feedback provided by college staff, and information found in existing\nguidelines for post-secondary institutions. The resulting understanding of issues related to sexual\nviolence on campus that emerged from this research informs the recommendations for Selkirk\nCollege’s sexual violence prevention and response strategy in various areas: identifying and\nutilizing a preferred language, gaining institutional buy-in and support, developing a peer-to-peer\ndelivery model, creating and rolling out an awareness campaign and designing and implementing\nevaluation mechanisms. Furthermore, this paper outlines three intervention approaches that are\neither currently in use at Selkirk College or are being considered for delivery to the campus\ncommunity in the near future: Bringing in the Bystander (BITB) training and supporting\nsurvivors education and healthy masculinities groups. While the recommendations found in this\npaper align with the approaches that many post-secondary institutions throughout the province\nare taking in order to meet the requirements of Bill 23 and to address issues related to sexual\nviolence, this project considers needs specific to the rural college-community of Selkirk College.\nThe perspectives and insights of Selkirk College staff members and the student body collected\nduring this study reflect the unique nature of this institution and have been incorporated into the\nsuggestions and recommendations this working paper offers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".