Rethinking confinement through Canada’s alternatives to detention program
Bibliographic record
Abstract
This article examines Canada’s immigration and detention system through its newly established alternatives to detention (ATD) program. As a program that relies rather extensively on mobile carceral technologies such as electronic monitoring and voice reporting, I argue that we should consider these alternatives not only as extensions of carceral apparatuses and the detention system but as pervasive, far-reaching, and more abstract manifestations of control and confinement. I introduce the term “techno-carcerality” to theorize how we might understand the shift from traditional modes of confinement to less traditional ones, grounded in mobile, electronic, and digital technologies. I contend that the use of mobile carceral technologies in the context of immigration and detention imposes physical, psychological, spatial, and data-driven modes of control and confinement in private, public, and digital space, which are veiled behind notions and misconceptions of increased freedom, mobility, and autonomy. While the ATD program may offer some better options than those available within detention centers or other carceral institutions, it is a program that involves immense surveillance dataveillance techniques that lead to profound carceral effects and anxieties, affecting everyday life in ways that experientially mimic and transcend conventional confinement.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.018 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".