The (Mis-)Uses of Analogy: Constructing and Challenging Crimmigration in Canada
Bibliographic record
Abstract
The concept of crimmigration is said to represent the blurring of criminal and immigration law, producing a sui genesis form of law within which sovereign power operates free from constitutional constraints. First articulated in the United States, crimmigration theory is just as (if not more so) prescriptive as descriptive. Centuries of immigration exceptionalism have blocked the applicability of due process rights to detention and deportation proceedings. This fact requires advocates to persuade courts that an immigration measure is criminal in nature in order to vindicate rights. There is a risk that crimmigration theory rests on, and makes use of, faulty doctrine that banishes immigration law from constitutional terrain unless it can be absorbed into the substance of criminal law. This chapter examines how crimmigration is constructed and challenged in Canada, where constitutional rights are applicable to immigration law qua immigration law. Relying on analogy, courts are most directly concerned with whether an immigration measure deprives someone of high priority interests in life, liberty or security of the person, and not whether a measure is criminal in nature. Whether courts will intervene, however, is a separate matter. This question is also answered by analogy: that between migrants and criminals. Conceptions of risk and danger always inform conceptions of rights, which courts in practice decline to recognize in all but a handful of cases. The different form crimmigration assumes in Canada masks substantively similar results.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.038 | 0.021 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".