Chapter 16 – A Brief History of the Brief History of Citizenship Revocation in Canada
Bibliographic record
Abstract
Four of the men convicted as part of the Toronto 18 prosecution were subject to citizenship revocation on grounds of terrorism. One of the four was born in Canada, and the other three immigrated to Canada and acquired citizenship through naturalization. I situate the politics of the four men’s citizenship revocation in legal and comparative context. Contemporary citizenship revocation policies, especially those invoked in the name of national security, serve both instrumental and symbolic goals. I argue that the citizenship revocation scheme enacted in Canada resonated primarily in the register of symbolic politics and lacked virtually any instrumental value related to national security. Its deployment against four of the Toronto 18 was always, and only, a calculated electoral tactic. I conclude by recounting the case of U.K.-Canadian Jack Letts in order to illustrate how citizenship revocation not only infringes fundamental human rights but is dysfunctional from the vantage point of international relations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.029 | 0.011 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".