The Evolving Common Law Jurisprudence Combatting the Threat of Terrorism in the United Kingdom, United States, and Canada
Bibliographic record
Abstract
Terrorism is a concept that defies a simple and straightforward legal definition. Therefore, it is not surprising to find that there is no Comprehensive Convention on Terrorism with a universally accepted definition of what constitutes “terrorism.” Consequently, States have devised their own definitions of what constitutes terrorism that are typically found in their criminal law. This raises the fundamental question of whether there is a convergence or divergence in jurisprudential trends on what constitutes terrorism among States? Presumably, a convergence in jurisprudential trends is more likely to contribute to combatting the threat of terrorism at the international and national levels. Accordingly, this article comparatively analyzes the definition of terrorism in three common law jurisdictions: the United Kingdom, the United States, and Canada. It finds that although there are a number of similarities in the definition of terrorism in these three States, they have significantly different definitions of what constitutes terrorism. The UK definition, ostensibly, has the broadest definition of terrorism of the three States. The US has, undoubtedly, the most unique, with separate definitions for “international terrorism” and “domestic terrorism.” Additionally, Canada has the most international definition of terrorism, drawing on 13 functional terrorism Conventions to define offenses such as hijacking, hostage taking, and bombing, etc. The second part of the article comparatively analyzes seven of the leading Supreme Court cases on terrorism in these three States. From the ratio or rationes decidendi in each of these cases, it draws out the twelve legal principles that underlie these judgements and finds that they are similar and overall consistent. The conclusion reached is that there is, at least in these three common law jurisdictions, an apparent convergence in jurisprudential trends in the law of terrorism. This augurs well for the development and emergence of a common definition of what constitutes terrorism at the international and transnational levels, as well as more rigorous and effective counter-terrorism laws and policies within and across States.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.019 | 0.031 |
| Scholarly communication | 0.020 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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".