Community Treatment Orders: Alternatives
Bibliographic record
Abstract
From its early drawing board outline to actual implementation, Bill 68 has been a major battleground between doctors, police officers, social workers, families and mental health groups. Bill 68 is also known as Brian’s Law, in reference to the Ottawa sportscaster Brian Smith, who was killed in 1995 by a man diagnosed as ‘paranoid schizophrenic. ’ Due to this incident and a few other high profile incidents involving the mentally ill, Bill 68 has been successfully sold as a way of protecting public safety. Five years ago, Bill-68 was introduced by the Ontario government with the intention of having a balance between patient rights and community safety. To name the law after a homicide victim, however, is unfair towards the larger population of individuals who suffer from mental disorders. “Unfortunately, our whole group has been painted…the very name of the law suggests that we are violent.”1 It is important to note that those who suffer from severe mental disorders are more often victims of violence rather than perpetrators of it. According to a Health Canada sponsored study, “there is no compelling scientific evidence to suggest that mental illness causes violence.”2 Some American studies have argued that at most, 4 percent of all violent incidents have any connection to mental illnesses.3
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.141 | 0.011 |
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".