Authoritarian Criminology and Racist Statecraft: Rationalizations for Racial Profiling, Carding and Legibilizing the Herd
Bibliographic record
Abstract
This essay is a discrete survey of administrative-authoritarian criminologists’ neutralizing techniques for justifying and aiding and abetting racial profiling in policing and, by inference, racialized ‘carding’. Principally focused on Canada and the US, material for this survey arises from the effort of administrative-authoritarian criminologists who claim to refute commissioned reports, case law and obiter dicta, government reports and scholarly research affirming racial profiling in particular and racial discrimination in the criminal legal system generally. Rooted in counter-colonial, anti-criminology and abolitionist epistemology my method of exposition is to turn the claims administrative-authoritarian criminologists hold to be true back onto criminology itself to see what account it provides for itself. Following the path worn by Hannah Arendt, I set out to demonstrate that in taking the effects of racial profiling and the legibilizing of ‘carding’ as objectively authoritarian-criminologists, administrative-authoritarian are irresponsible in the exercise of judgment to true ideas.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.089 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".