The Intersection of Racialized Crime and the Forced Removal of ‘Foreign Criminals’ from Canada: A Critical Analysis
Bibliographic record
Abstract
This paper presents a critical analysis of the intersectionality of race and crime by examining the criminalization of the Black community in the Greater Toronto Area. It contextualizes the removal of ‘foreign criminals’ through Canadian deportation policies, focusing on the evolution of Bill C-44, the “danger to the public” clause, and its impact on the Afro-Caribbean community. The use of qualitative interviews involving three service providers in Trinidad and Tobago who work with deportees, as well as a young man who was recently deported from Canada, are used to highlight the negative impact of Canadian deportation policies on deported persons removed from the nation-state, as well as on the receiving country. This paper draws attention to the ways in which intersecting oppressions of race, class, spatial location, and citizenship status single out racial ‘minorities’ for increased surveillance, and justifies their perceived criminality.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.064 | 0.033 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".