Mitigating and bordering: The dual nature of Canadian collateral consequences of conviction
Bibliographic record
Abstract
Roughly, 13% of Canada’s adult population has some kind of criminal record. Collateral consequences stemming from a criminal record are wide-ranging, from formal restrictions to more informal forms of exclusions. In this article, I argue that Canada exhibits a distinct and dual approach with regard to collateral consequences. A commitment to principles, such as human dignity, rehabilitation, proportionality and individualisation in sentencing, especially by the courts, has increasingly mitigated the impact of collateral consequences in many areas. Yet, these interventions to limit collateral consequences have been far more uncommon for immigration-related collateral consequences, where the impact of a criminal conviction has only expanded in the last decades. This suggests the centrality of the criminalisation of migration in Canada’s bordering regime and its role in drawing boundaries between desirable and undesirable migrants. This dual nature of collateral consequences also sends a message about who is, and who is not, deserving of second chances.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".