Whitewashing Criminal Justice in Canada: Preventing Research through Data Suppression
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it