International Counter-Terrorism Regulation and Citizenship Stripping Laws - Reinforcing Legal Exceptionalism
Bibliographic record
Abstract
In this article, we conduct a timely analysis of international counter-terrorism law and its relation to domestic measures like citizenship stripping in light of the exceptionalist and extra-legal tendencies of the former. We highlight the Ping-Pong effect between international and domestic counter-terrorism laws showing that domestic and international law mutually reinforces each other’s exceptionality. We argue, first, that the international law framework for counter-terrorism measures exhibits the characteristics of a ‘model of accommodation’, fostering an extra-ordinary legal approach that is inspired by domestic law designs of counterterrorism law; second, that international law further enables and encourages domestic law to adopt extra-ordinary or even extra-legal measures in the field of counter-terrorism; and third conversely, domestic measures like citizenship-stripping laws more broadly affect general international law by contributing to the normalization of extra-ordinary legal measures. In this regard, we discuss recent domestic citizenship-stripping laws as an expression of a renewed emphasis on exceptionalism and extra-legalization of counter-terrorism measures. Considering citizenship-stripping laws enacted in various jurisdictions, including Australia, Canada and the UK, we argue that by justifying exceptionalist citizenship-stripping laws as permissible under both, international and domestic law, states will permanently affect the concept of citizenship nationally and internationally.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".