Violent crime, hate speech or terrorism? How Canada views and prosecutes far-right extremism (2001–2019)
Bibliographic record
Abstract
Fifty-six individuals were charged with terrorism between December 2001 when Canada first enacted its antiterrorism criminal offences and December 2019. Not a single such individual was associated with a far-right group or espoused a far-right ideology. Over the same period of time, Canada saw a rise in far-right violence and crime, including several deadly attacks that raised the spectre of terrorism. This article seeks to identify why terrorism has not been associated with the activities of those on the far right, how Canada has prosecuted far-right violence if not for terrorism and what the implications are for Canada’s criminal prosecutions going forward. It finds that since December 2001 all publicly reported hate speech cases and cases where an individual’s sentence was aggravated for hate have involved individuals espousing far-right rhetoric; likewise, all but one case where the media raised the spectre of terrorism but no such charge ensued can be described as being motivated by far-right ideation. In the result, Canadian law punishes more seriously Al-Qaida (AQ)-inspired extremism than far-right extremism, while stigmatizing the former more than the latter. The time has thus come to tackle head-on the concept of ideology in Canadian criminal law, and how the law treats various ideologies.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".