CHOICES AND APPROACHES: ANTI-TERRORISM LAW AND CIVIL SOCIETY IN THE UNITED STATES AND THE UNITED KINGDOM AFTER SEPTEMBER 11
Bibliographic record
Abstract
In Canada, as in most nations, law and policy tied closely to counter-terrorism are among the mechanisms for regulating the spaces in which civil society functions. Since the terrorist attacks of 11 September 2001, these mechanisms have increased in scope and importance. The United States has adopted a prosecution-based strategy in these matters, along with detailed guidance for foundations and other non-profit organizations. The United Kingdom has retained and enhanced a charitable regulation system as a first line of defence, along with prosecutions and other steps. Because the us and uk governments have acted in different ways, civil-society groups in those countries have faced different impacts in dealing with their own liberty to operate and in representing and advocating for the broader liberties of their fellow citizens. Given that Canada's approach to anti-terrorism law and policy will be debated anew in the context of the 2010 report of the Commission of Inquiry into the Investigation of the Bombing of Air India Flight 182, as will related issues of terrorist ties to civil-society organizations, this article discusses the different approaches that have emerged in the United States and the United Kingdom, some of the problems and challenges associated with each approach, and some potential implications for Canada.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.041 | 0.064 |
| Scholarly communication | 0.032 | 0.009 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.015 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".