Managing the Fear of the Tsunami: Canada's Proposed Policy to Detain Boat People and Lessons Learned from the United States' Detention Policies
Bibliographic record
Abstract
The Canadian government has proposed legislation to deter and prevent smuggling of people into Canada. In doing so, it created new powers for immigration officials to indefinitely detain foreign nationals arriving by boat. This legislative response is a reflection of panic and fear of boat people. The Canadian government, through Bill C-4, is aggregating all boat people as terrorists, smugglers and traffickers, deviants, and criminals. The government suggests that boat people should be detained. In examining the merits of this policy, this paper looks to Canada's neighbor, the United States, to draw some lessons from their practice of detaining migrants. Accepting such a practice in Canada would mean criminalizing an administrative process, ignoring less invasive and more humane alternatives, and would not necessarily decrease costs for Canadians. Detaining migrants also does not lead to proportional or functional results, as the government hopes it will, such as deterring and preventing undesirable behavior and undesirable persons. This paper calls for a nuanced examination of who boat people are, and a measured response to dealing with those arriving by boat.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.008 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.010 | 0.008 |
| 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".