Domestic Terrorism Classification in the United States v.\nCanada and the United Kingdom
Bibliographic record
Abstract
For the past two decades, discourse on terrorism (both global and domestic) has been commonplace throughout the international sphere. Following the attacks on September 11, 2001, many nations have followed suit in launching counterterrorism operations to identify and prevent attacks by both radical groups and lone actors. While the common narrative has focused on “why” terrorist actors commit heinous acts and “how” to best prevent future incidents from emerging, it is important to analyze the legal nuances between prosecuting domestic versus international terrorists. With the rise on “homegrown” domestic lone actors, nations have had to reevaluate and adapt counterterrorism statutes and legal systems to apply to actors who are often unaffiliated with previously identified terrorist organizations. Some nations have been more successful than others. This Note seeks to explore the nuances of the counterterrorism strategies and legal frameworks used to prosecute domestic terrorism of three nations—the United States, Canada, and the United Kingdom—and the estimated success rates of these systems.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".