Excluded in the “best interests of the state”: treatment of minor Canadians under immigration and refugee protection laws
Bibliographic record
Abstract
In this thesis, I focus on minor Canadian children born to non-citizens detained under the Immigration and Refugee Protection Act of 2001. My argument is that the standard of treatment meted out to such children is different to that of Canadian children with a detained parent in the Canadian prison system. While both actions are claimed to be “in the best interests of the child,” I assert that the law treats best interests of Canadian children differently when their parents are detained under immigration laws. Tracing a general evolution of the concept of “best interests of the child” (BIOC) laid down in Article 3 (1) of the United Nations Convention on the Rights of the Child, I analyze its reception and application in Canadian immigration laws. After that, I contrast the same with the application of this principle under child protection laws in Ontario and British Columbia. These two provinces have two of the three Immigration Holding Centres (IHC) in Canada and therefore permit the best comparison. I analyze the discourses surrounding detention of non-citizens’ children, including laws, policies, guidelines, oral/written reasonings to understand the process of legitimization involved and the inherent contradictions within them. Applying Lajos Brons’ concept of “sophisticated othering” I argue that Canada indulges in the process of “othering” when it interprets a legal concept (BIOC) differently for two similarly placed subjects.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.032 | 0.018 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".