The Double Punishment of Criminal Inadmissibility for Immigrants
Bibliographic record
Abstract
Canada bars non-citizens from entering or staying in the country for a number of reasons, including for what immigration law treats as “criminality”, “serious criminality” or “organized criminality”. Criminal inadmissibility– including for rather minor criminalized acts–raises a number of concerns for those who are targeted, but also for border criminologists, prisoners’ rights activists and migrant justice organizers. In this article, we discuss inadmissibility for “criminality” and “serious criminality” as: 1) a populist rhetorical move put forth by politicians promoting tough-on-crime / tough-on-immigration policies; 2) a form of double punishment that starts before deportation even takes place (and regardless of whether it does); and 3) a discretionary tactic in the policing toolbox to incapacitate people who are deemed undesirable. We illustrate the consequences of criminal inadmissibility by drawing from the experience of one of the co-authors who is facing deportation on this ground.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.034 | 0.016 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.010 |
| 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".