Authoritarian Criminology and the Racial Profiling Debate in Canada: Scientism as Epistemic Violence
Bibliographic record
Abstract
The scholarly debate about racial profiling in Canada centres on the attack or defence of methods and conclusions. To date, the scholarly literature in Canada has largely excluded the debate about racial profiling as itself a site of inquiry. Focusing on the 2003 Canadian Journal of Criminology and Criminal Justice (CJCCJ) special colloquy that responded to the Toronto Star’s claims that the Metro Toronto systemically engage in racial profiling, I use Agozino’s critique of imperialist reason and Tauri’s concept of authoritarian criminology to demystify how the CJCCJ and its authoritarian respondents engaged in a positivistic agenda setting ‘discursive formation’ rooted in scientific rationality. Further, I draw on the death of (white) sociology tradition to demonstrate that this discursive formation, rooted in the pretensions of scientific rationalism, constitutes both epistemic violence toward the colonized and people ‘of color’ while certifying scientific veracity as an embodied property of White middle class and system serving males. The tactic of criminological and legal positivists in this debate is to disqualify, subordinate, ridicule and ultimately invalidate the truth claims of racial profiling victims.
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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.069 | 0.087 |
| Scholarly communication | 0.030 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".