Bibliographic record
Abstract
The terrorist attacks of 11 September 2001 have resulted in the expansion of anti-terrorism laws throughout the globe. The international and domestic reaction to these attacks constitute a type of horrible natural experiment in the migration of constitutional and anti-constitutional ideas. In this chapter, I will attempt to provide insight into the complexity of the migration of constitutional ideas with respect to anti-terrorism laws by examining the influence of the definition of terrorism in Britain's Terrorism Act 2000 on the post-9/11 development of anti-terrorism laws in Australia, Canada, Hong Kong, Indonesia, South Africa, and the United States, as well as the role that domestic law, politics, and history played in producing variations in the definition of terrorism in each country. As Kim Lane Scheppele argues in her contribution to this collection, international law, and in particular the UN Security Council Resolution 1373, helped shape the worldwide expansion of anti-terrorism laws after the 9/11 terrorist attacks. At the same time, as Professor Scheppele notes, Security Resolution 1373 made no attempt to define terrorism and a universal definition of terrorism has so far eluded the international community. A failure to define terrorism in international law allowed various local agendas to enter into the definition of terrorism. Although, as Professor Scheppele suggests, the local agenda sometimes helped produce more repressive laws, at other times, it restrained the new international mandate to combat terrorism.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".