Bibliographic record
Abstract
This paper reviews the legislative history and application of the Criminal Code’s definition of terrorist activity to trace how the “motive clause” reinforces systemic racism within Canada’s criminal justice system. By outlining this process, this paper argues that the motive clause contributes to a dynamic that racializes terror offences as a specific type of criminal offence committed by racialized individuals—marking terrorism as a unique social characteristic of racialized communities. This occurs mainly due to the legislative requirement to prosecute the ideas of accused persons, which, in practice, has increased the likelihood of courts admitting otherwise prejudicial evidence against the accused and the problematic ways in which expert evidence has (or has not) been used in terrorism trials. Although discrimination may not be an inevitable or intended outcome of the drafted legislation, it creates a framework that encourages discriminatory prosecutorial strategies, facilitates bias in the admission and treatment of some evidence, and potentially contributes to the exclusive use of the provisions against racialized communities specifically.
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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.033 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| 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".