Criminalizing Terrorist Babble: Canada's Dubious New Terrorist Speech Crime
Bibliographic record
Abstract
Before the introduction of Bill C-51, the Canadian government expressed interest in a terrorism “glorification” offence, responding to Internet materials regarded by officials as terrorist propaganda and as promoting “radicalization.” Bill C-51 introduces a slightly less broad terrorism offence that applies to those who knowingly promote or advocate “terrorism offences in general” while knowing or being reckless as to whether terrorism offences “may be committed as a result of such communication.” This article addresses the merits of these new speech-based terrorism offences. It includes analyses of: the sociological data concerning radicalization and “radicalization to violence”; existing offences that apply to speech associated with terrorism; comparative experience with glorification crimes; and the restraints that the Charter would place on any similar Canadian law. We conclude that a glorification offence would be ill-suited to Canada’s social and legal environment and that even the slightly more restrained new advocacy offence is flawed. This is especially true for Charter purposes given the less restrictive alternative of applying existing terrorism and other criminal offences to hate speech and speech that incites, threatens, or facilitates terrorism. We are also concerned that the new speech offence could have counter-productive practical public safety effects. We favour that part of Bill C-51 that allows for court-ordered deletion of material on the Internet that was criminal before Bill C-51, namely material that counsels the commission of terrorism offences. However, Bill C-51’s broader provision that allows for the deletion of material that “advocates or promotes the commission of terrorism offences in general” suffers the same flaws as its enactment of a new offence for communicating such statements.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".