Manufacturing “Terrorists”: Refugees, National Security and Canadian - Part 2
Bibliographic record
Abstract
The overarching objective of this paper is to provide a critical appraisal of the anti-terrorism provisions of Canada’s Immigration Act. The impact of these measures on refugees is the primary concern of this inquiry, but the author’s observations are relevant to the situation of other categories of non-citizens as well. Part 1 of the essay, published in the previous issue of Refuge, began by considering international efforts to address “terrorism,” the relevance of international humanitarian law to an assessment of acts of “terror,” and the nature of contemporary discourse on terrorism. The evolution of the current “admissibility” provisions in Canadian immigration law was examined with particular reference to national security threats and “terrorism.” In part 2, the author focuses on the role played by Canada’s Federal Court in legitimizing the national security scheme. The tensions in the current jurisprudence are considered with a more in-depth analysis of Suresh v. Minister of Citizenship and Immigration, a case pending before the Canadian Supreme Court. The paper concludes with suggestions for restoring human rights for refugees while safeguarding a genuine public interest in security.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Science and technology studies | 0.024 | 0.021 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".