A Coordinated Judicial Response to Counter-Terrorism?: Counter-Examples
Bibliographic record
Abstract
The chapter assesses Eyal Benvenisti’s claim that courts from prominent democratic states have reacted consistently to counter-terrorism measures, coordinating outcomes across national jurisdictions. This claim is conjoined with another, namely that the availability of identical or similar norms (grounded in international law and human rights law) has facilitated this coordination effort. The chapter criticises the suggested phenomena of a ‘globally coordinated move’ on the part of ‘national courts from prominent democratic states’ by way of counter-examples. The counter-examples are drawn from cases that are enlisted by Benvenisti as examples of this inter-judicial coordination effort, namely the Supreme Court of Canada’s 2007 decision in Charkaoui and the House of Lords 2004 Belmarsh decision (A v. Secretary of State for the Home Department). The relationship of Charkaoui to the English and American decisions in Hardial Singh and Zadvydas is also assessed. The argument is that key instances of reliance on comparative authority and international human rights law in Charkaoui (including claims of compatibility with Belmarsh), while not simply decorative, do not maintain the level of consistency between the national courts needed to support claims of an ‘inter-judicial coordination effort’ in response to state counter-terrorism measures.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".