Whitewashing Criminal Justice in Canada: Preventing Research through Data Suppression
Bibliographic record
Abstract
Race and racism have long played an important role in Canadian law and continue to do so. However, conducting research on race and criminal justice in Canada is difficult given the lack of readily available data that include information about race. We show that data on the race of victims and accused persons are being suppressed by police organizations in Canada and argue that suppression of race prevents quantitative anti-racism research while not preventing the use of these data by the police for racial profiling. We also argue that when powerful institutions, such as the police, have knowledge that they keep secret or refuse to discover, it serves the interests of those institutions at the expense of the public. Fears that reporting of racial data will result in racial profiling or the stigmatization of racialized communities are not assuaged by the repression of this information. Stigmatization may still occur, and racial profiling can continue to happen, but without public knowledge. Quantitative anti-racist research requires consistent, institutionalized reporting of race data through all aspects of Canadian justice. We outline what data are available, what data are needed, and where consistency is lacking. It is argued that institutional preferences for white-washed data, with race and ethnicity removed, should be subrogated to transparency.
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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 | 0.330 | 0.502 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.021 |
| Science and technology studies | 0.030 | 0.030 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.009 | 0.018 |
| Research integrity | 0.005 | 0.009 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".