Bibliographic record
Abstract
Normative theory has engaged in a robust debate on the ethics of immigration admissions in liberal democracies. Yet, its scholars are mostly silent on the conditions and cascading harms caused by immigration detention, or the state&s;s incarceration of non-citizens for immigration-related reasons. To remedy this oversight, we here present a case study of the relatively minimalist Canadian detention system and examine its real-world mechanics through a normative lens. We find that detention is both an integral part of the contemporary immigration enforcement system and morally wrong. By closely examining this case study, and layering our findings onto the normative debate, we hope to clarify morality, justice, and political problems that normative scholars have overlooked in debates on borders, state violence, and the human right to migrate. We argue that detention is a key moral problem left unanswered by normative political theorists examining the ethics of immigration admissions.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.074 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".