Bibliographic record
Abstract
This book does more than provide the reader with an extremely thorough analysis of the debates that surrounded the introduction of a broad range of anti-terrorism measures in Canada including the Anti-Terroism Act. 2 Kent Roach is in the unique position of being a legal scholar who is equipped to apply a social science analysis to these issues.Social positions, societal definitions, and politics are all very relevant to not only the making of laws but also the interpretation of them.Law is seen to be relevant only in context-more often serving a symbolic function rather than a strictly utilitarian one.The advantage of topics like terrorism, organized crime, or any other ill-defined concept is that they can metamorphose into a very different type, and degree, of threat under the pens (or the computer equivalent) of writers, representing either different disciplines or different vested interests-or both.All national security matters have traditionally been cloaked in secrecy.How does one know if Canadian responses will work effectively to curb the threats from organized crime, corruption, or terrorism if one does not know accurately what those threats are?One is not particularly reassured by the comments of former Minister of Justice Anne McLellan in response to the various criticisms of the reach of the proposed antiterrorism bill: "Gosh, you know, I wish you were in my shoes for 24 hours.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.046 | 0.022 |
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".