The ‘problem’ of abuse in Ontario’s Social Inclusion Act: A critical exploration
Bibliographic record
Abstract
Employing Carol Bacchi’s What’s the problem? approach, this article examines the abuse policy recently implemented through the Social Inclusion Act of Ontario, Canada’s developmental services sector (DSS), and how it constitutes sexual abuse of people with intellectual disabilities as a policy problem. Politically committed to preventing and addressing abuse, we examine how sexual abuse is ‘given shape’ in the policy and its compliance training materials, and how the policy’s mandatory police reporting requirement ‘subjectifies’ victims according to a taken-for-granted legal ‘worldview’ that presumes justice is achieved through criminalisation. We also demonstrate the everyday ‘deleterious effects’ of this policy in relation to how it leaves both support for sexuality and the long-standing crisis management approach of Ontario’s DSS unproblematised. This analysis calls into question the abuse policy of the Social Inclusion Act and demonstrates the pressing need to re-problematise abuse prevention and redress for people with intellectual disabilities.
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 | 0.019 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.052 | 0.087 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".