We're Here for Who? A Post-Structural Policy Analysis of Sexual Violence Policies on Ontario University Campuses
Bibliographic record
Abstract
In September 2016, the provincial government of Ontario required all publicly funded universities and colleges in the province to create standalone sexual violence policies through the introduction of the Sexual Violence and Harassment Action Plan Act, formerly known as Bill 132: Sexual Violence and Harassment Action Plan Act (Supporting Survivors and Challenging Sexual Violence and Harassment). Despite its rollout over 5 years ago, sexual violence on post-secondary campuses continues at an alarming rate. Through an exploratory case study of the Act and 5 Ontario universities, this research uncovers the gaps in current sexual violence policies by exploring what accounts for different campus contexts in relation to sexual violence and rape culture if publicly funded universities in Ontario are all mandated by similar policies? Findings focus on failure to adequately prevent and respond to sexual violence and rape culture due to the provincial government neoliberal framing and lack of intersectional approaches. Policies that work to prevent societal issues such as sexual violence must incorporate an intersectional approach that reframes current narratives, provide consistent and transparent responses across the sector, and hold individuals accountable for their actions.
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 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".