Bibliographic record
Abstract
Canada’s approach to proscription differs from that of other Westminster democracies. After the negative example of listing in the October Crisis, 1970 and with the subsequent advent of a constitutional bill of rights, Canada does not ban organizations; instead it penalizes certain forms of conduct, above mere membership, with terrorist groups. “Terrorist groups” include entities listed proactively by the executive, but also entities that meet a functional definition in Canadian criminal law. In practice, the latter, functionally-defined terrorist groups have figured in most terrorism prosecutions—only a few cases have involved listed groups. With the new focus on Daesh (a listed group), that may begin to change. However, executive listing raises unresolved constitutional doubts in Canada, prompting concerns that reliance on proscription may be more trouble than it is worth. Listing has also been used with respect to individuals, but such listings in Canada have already produced false positives, perhaps because of the due process deficits of listing by the executive. In many respects, therefore, terrorist group listing is yesterday’s law, problematic and of marginal utility. There may be reasons of administrative expediency to preserve listing, but the tool is more doubtful when used as a precursor to criminal prosecutions.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.025 | 0.010 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".