Stand by Me: Viewing Bystander Intervention Programming through an Intersectional Lens
Bibliographic record
Abstract
Post-secondary institutions are increasingly choosing or being compelled to come to terms with their institutional responsibilities with respect to sexual violence and rape culture. In several jurisdictions, including Manitoba, Ontario, and British Columbia, post-secondary institutions are now legally mandated to develop sexual violence policies, and outside of these jurisdictions other post-secondary institutions are choosing to develop sexual violence policies and programming. While the types of programs and policies implemented by different institutions have varied, bystander intervention programs have been adopted or are being considered in a number of places. Considering this growing reliance on bystander intervention programs at some post-secondary institutions, combined with the increasing diversity of the student body, it is important that bystander intervention programming take an intersectional approach.This chapter will explore the ways in which bystander intervention programming can apply an intersectional approach to better address the needs and experiences of students at a variety of social locations. We highlight and discuss seven design and implementation aspects of bystander intervention programming that could benefit from an intersectional approach including: identifying patterns of victimization and perpetration; structural critiques; intervention strategies; audience; bystander bias; selection and training of facilitators; and empirical data relied on to validate bystander programming.
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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 | 0.017 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.019 | 0.054 |
| Scholarly communication | 0.023 | 0.024 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".