Bibliographic record
Abstract
It is commonly believed that a state facing a terrorist threat responds with severe legislation that compromises civil liberties in favour of national security. Roger Douglas compares responses to terrorism by five liberal democracies— the United States, the United Kingdom, Canada, Australia, and New Zealand— over the past 15 years. He examines each nation's development and implementation of counterterrorism law, specifically in the areas of information gathering, the definition of terrorist offenses, due process for the accused, detention, and torture and other forms of coercive questioning. Douglas finds that terrorist attacks elicit pressures for quick responses, which often allow national governments to accrue additional powers. But emergencies are neither a necessary nor a sufficient condition for such laws, which may persist even after fears have eased. He argues that responses are influenced by institutional interests and prior beliefs and are complicated when the exigencies of office and beliefs point in different directions. He also argues that citizens are wary of government's impingement on civil liberties and that courts exercise their capacity to restrain the legislative and executive branches. Douglas concludes that the worst anti-terror excesses have taken place outside of, rather than within, the law and that the legacy of 9/11 includes both laws that expand government powers and judicial decisions that limit those very powers. This title was made Open Access by libraries from around the world through Knowledge Unlatched.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.041 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".