Bibliographic record
Abstract
In this article, I propose ten specific ways in which concrete improvements can be made to Canadian anti-terrorism law and the national security components of Canadian immigration law. Although Canada's criminal anti-terrorism law is more restrained than that of some other countries, including recent laws and proposals in the United Kingdom, this should not be taken as a sign that there is no room for improvement. It should also not be assumed that improvements to the law will increase rights and freedoms at the expense of security. Indeed some of the improvements to Canadian law that are suggested below could assist the state in focusing on the most serious threats to our security. Some could also help prevent the wrongful conviction or detention of those who are not terrorists (which may allow the guilty to go free) while ensuring that Canada's response to terrorism is properly focused and proportionate.
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 | 0.023 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 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".