How Do Persons Found NCRMD and Identified as Indigenous Differ from Other Persons Found NCRMD: Profiles, Trajectories, and Outcomes
Bibliographic record
Abstract
Indigenous individuals are vastly over-represented among people incarcerated in Canada. We collected extensive clinical information and outcome data from Review Board (RB) files and obtained lifetime criminal records for 1800 individuals found Not Criminally Responsible on Account of Mental Disorder (NCRMD) in BC ( n = 222), ON ( n = 484), and QC ( n = 1094). Indigenous and non-Indigenous people were compared on (a) socio-demographic, clinical, and criminal histories; (b) index offenses; (c) processing by the RB; and (d) recidivism. Compared to published rates of the disproportionate incarceration of Indigenous people in prisons in Canada (30%), just 3.9% of people in custody with an NCRMD finding were identified as Indigenous. Compared to non-Indigenous people, Indigenous people had higher rates of substance use disorders, personality disorders, and lower rates of mood disorders at verdict and came from low population density neighborhoods but high population density homes. Indigenous individuals were detained in custody longer and remained under supervision longer than non-Indigenous individuals but recidivated at similar rates. Criminal histories, mental health characteristics, and index offenses of Indigenous people found NCRMD were similar to non-Indigenous people found NCRMD. Further research is required to determine if seriously mentally ill Indigenous people who come into contact with the justice system are considered for the NCRMD defense similarly to non-Indigenous people and to explore why Indigenous individuals receive more restrictive dispositions, yet time to reoffending is similar.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".