Sexual violence against students with disabilities on campus: a review of resources and accessibilities
Bibliographic record
Abstract
Sexual violence against students with disabilities is like a submerged iceberg hiding beneath the surface of educational settings, unnoticed and ignored. On college campuses, students with disabilities experience more sexual assaults on campus than their peers without disabilities, yet there is a noticeable lack of discourse on prevention strategies and the potential benefits of increased accessibility (Burczycka, 2020; Busby K. & Birenbaum J., 2020). Most Canadian universities fail to provide accommodation for disclosure and policy implementation, which are their legal duty to ensure trauma-informed services for the students (Chugani et al., 2021; Fread, 2021). This denial of accommodation to people with disabilities disregards not only constitutes a violation of legal obligation but also infringes upon their inherent human rights. This barrier to justice has been a product of long-standing systemic oppression of one of the largest minority groups – people with disabilities. Research demonstrates that, instituting inclusive policy, accommodation and accessible services can eliminate the sexual violence incidents on campus (Chugani et al., 2021; Findley et al., 2016; Holloway, 2019). Moreover, these steps towards inclusivity would be meaningful to change social attitude towards people with disabilities.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".