Classification and Collection of Terrorism Incident Data in Canada
Bibliographic record
Abstract
Canada is far from immune from the pressing global terrorism threat. Despite low base rates for documented attacks, it would be inaccurate to measure terrorism simply by the number of incidents investigated by authorities. This caution exists for two reasons. First, there is good reason to question current statistics as the majority of incidents either go unreported or are categorized under other labels. Second, every act carries a disproportionate harm. Even foiled attacks increase the level of fear, heighten tension between different groups, and can fragment communities. Social harm can be greater than the crime because it can affect individuals, groups and even nations. For these broad reasons a vigorous response is warranted. Specialized units have been created in many law enforcement organizations, new legislation has emerged and the collection of terrorism-related information is well at hand. Or is it? This paper presents compelling arguments that acts of terrorism are far more prolific than Canadian statistics suggest. Furthermore, this situation will continue to exist because the true nature of terrorism is concealed by systemic failures strikingly similar to those which historically masked the problem of both partner abuse and hate crime.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.026 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".