Challenges for Democracies in Responding to Terrorism: A View from Canada and Israel
Bibliographic record
Abstract
The November 2015 terrorist attacks at the Bataclan concert hall and sidewalk cafés in Paris, the March 2016 bombing at the Brussels airport, and the mass shooting at the Pulse nightclub in Orlando, Fla. in June 2016 are just a few examples of the horror and loss of life inflicted on innocent civilians by individuals affiliated with, or supporting the Islamic State of Iraq and al-Sham (ISIS). Canada is not immune: in October 2014 two ISIS-inspired attacks, one in Saint-Jean-sur-Richelieu, Que. and one in Ottawa, resulted in the deaths of Warrant Officer Patrice Vincent and Cpl. Nathan Cirillo. More recently, ISIS vowed to make the month of Ramadan a bloodbath in Europe and America.2 Heightened awareness, attention, and concern among western democracies surround the issue of terrorism on home soil. And there are serious challenges in addressing such threats.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.047 | 0.020 |
| Scholarly communication | 0.018 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".