Race and Incarceration: The Representation and Characteristics of Black People in Provincial Correctional Facilities in Ontario, Canada
Bibliographic record
Abstract
Racially disaggregated incarceration data are an important indicator of population health and well-being, but are lacking in the Canadian context. We aimed to describe incarceration rates and proportions of Black people who experienced incarceration in Ontario, Canada during 2010 using population-based data. We used correctional administrative data for all 45,956 men and 6,357 women released from provincial correctional facilities in Ontario in 2010, including self-reported race data. Using 2006 Ontario Census data on the population size for race and age categories, we calculated and compared incarceration rates and proportions of the population experiencing incarceration by age, sex, and race groups using chi-square tests. In this first Canadian study presenting detailed incarceration rates by race, we found substantial over-representation of Black men in provincial correctional facilities in Ontario. We also found that a large proportion of Black men experience incarceration. In addition to further research, evidence-based action is needed to prevent exposure to criminogenic factors for Black people and to address the inequitable treatment of Black people within the criminal justice system.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".