Years of life lost to incarceration: inequities between Aboriginal and non-Aboriginal Canadians
Bibliographic record
Abstract
BACKGROUND: Aboriginal representation in Canadian correctional institutions has increased rapidly over the past decade. We calculated "years of life lost to incarceration" for Aboriginal and non-Aboriginal Canadians. METHODS: Incarceration data from provincial databases were used conjointly with demographic data to estimate rates of incarceration and years of life lost to provincial incarceration in (BC) and federal incarceration, by Aboriginal status. We used the Sullivan method to estimate the years of life lost to incarceration. RESULTS: Aboriginal males can expect to spend approximately 3.6 months in federal prison and within BC spend an average of 3.2 months in custody in the provincial penal system. Aboriginal Canadians on average spend more time in custody than their non-Aboriginal counterparts. The ratio of the Aboriginal incarceration rate to the non-Aboriginal incarceration rate ranged from a low of 4.28 in Newfoundland and Labrador to a high of 25.93 in Saskatchewan. Rates of incarceration at the provincial level were highest among Aboriginals in Manitoba with an estimated rate of 1377.6 individuals in prison per 100,000 population (95% confidence interval [CI]: 1311.8-1443.4). CONCLUSIONS: The results indicate substantial differences in life years lost to incarceration for Aboriginal versus non-Aboriginal Canadians. In light of on-going prison expansion in Canada, future research and policy attention should be paid to the public health consequences of incarceration, particularly among Aboriginal Canadians.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".