The Price of Prevention: Anti-Terrorism Pre-Crime Measures and International Human Rights Law,
Bibliographic record
Abstract
How far can law go to prevent violent acts of terrorism from happening? This Article examines the response by a number of Western democratic States to that question. These States have enacted special legal mechanisms that can be called ‘anti-terrorist pre-crime measures.’ Anti-terrorist pre-crime measures, or ATPCMs for short, are conditions or restrictions imposed on a person by law enforcement authorities as the outcome of a legal process set up to identify and neutralize potential sources of terrorist activity before it occurs. The issue is whether the ATCPMs regimes in existence today comply with the corresponding States’ international obligations under human rights law because, by virtue of their preventative mission, these regimes operate outside, or on the fringes of, the ordinary criminal justice systems in the democratic societies that deploy them. Despite the operation of ATPCMs regimes in robust democracies like the United Kingdom, Canada, Australia and, potentially, the United States, they surprisingly have not been the subject of recent international scrutiny or systematic comparative study. This Article fills both gaps. On the one hand, it documents how the national legal frameworks in the aforementioned countries design and deploy anti-terrorist pre-crime measures, as well as how those measures function in practice. On the other, the Article canvasses the relevant international legal framework to identify not just which human rights are implicated by the operation of ATPCMs regimes, but also how those rights are impacted by it. The Article then applies this normative framework to the domestic counter-terrorism initiatives studied to ascertain how, and the extent to which, the respective ATPCMs regimes can be said to comply with human rights law. Significant insights can be derived from this exercise for other countries like the United States that authorize or contemplate implementing ATPCMs.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.034 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".