Truth commissions on disability institutions: towards a disability truth and repair framework
Bibliographic record
Abstract
Abstract Article 19 of the Convention on the Rights of Persons with Disability provides for the right to live independently and be included in the community. The UN Committee on the Rights of Persons with Disabilities clarifies that this right requires States parties to undertake deinstitutionalization that extends to providing ‘remedies, reparations, and redress’ for institutionalization, including through establishment of truth commissions. In this article I argue for the development of a Disability Truth and Repair Framework to support future design and critical evaluation of truth commissions on disability institutions. I map out a series of conceptual and practical considerations that can inform the framework, by reference to philosophical scholarship on moral repair and critical disability studies scholarship on institutions. Considerations include temporal, familial, and cultural dynamics of institutionalization, connections between institutionalization and other dynamics of oppression and settler colonialism, professional, government, and charity power, and diverse lived experience and accessibility needs.
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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 | 0.057 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.012 | 0.114 |
| Scholarly communication | 0.025 | 0.033 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".