LESSONS TO BE LEARNED: THE DECISION OF THE SOCIAL BENEFITS TRIBUNAL IN TRANCHEMONTAGNE V. ONTARIO (DIRECTOR, DISABILITY SUPPORT PROGRAM)
Bibliographic record
Abstract
In April 2009, the Ontario Social Benefits Tribunal ended a 10 years battle for appellants, Norman Werbeski and Robert Tranchemontagne in its deliberation that the Ontario Disability Supports Program Act violated their rights as enshrined in the Ontario Human Rights Code when it failed to recognize their alcoholism as a disability. Drawing specific excerpts from the Respondents (Ontario government) defence and the Social Benefit Tribunal’s decision, the essay looks at how government policy affects persons with substance addiction and argues that Section 5(2) of the ODSPA places burdens on substance abusers and contributes significantly to their social stigma. The outcomes of excluding substance addiction from the United States Social Assistance Programmes are reviewed and referred to in analyzing the Respondent’s claims. The conclusion highlights how this case reflects key steps towards greater inclusion for persons with disabilities and in particular for those whose disability is addiction and alcoholism. Keywords: Social Benefits Tribunal, Ontario Disability Supports Program Act, alcoholism, disability, stigma
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.047 | 0.045 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".