The forced removal of the 'criminally inadmissible' as punishment: a double jeopardy
Bibliographic record
Abstract
Canada bars non-citizens from entering or staying in the country for a number of reasons, including for conduct considered to be ‘criminally inadmissible’. Informed by a critical race theory, this literature review and analysis of recent Canadian deportation data highlights the ways in which the double punishment of forced removal following the incarceration of non-citizens extends the Canadian criminal justice system which disproportionately incarcerates Black bodies. Focusing on the evolution of Canadian deportation policies, this paper contributes to the criminological and migrant studies literature by developing our understanding of the deportation process in Canada and its highly politicized and racially discriminatory effects, specifically on the Afro-Caribbean community. It conceptualizes deportation as a form of double punishment and illustrative of how immigration law and criminal law reinforce each other. This paper considers the grave implications of Canadian deportation policies on those who not only experience it, but also on the families and communities of deported persons and on the safety and security of the country which they return to. Keywords: Canada, Criminality, Race, Deportation, Punishment.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".