Privacy, Crime, and Terror: Legal Rights and Security in a Time of Peril Stanley A. Cohen (Markham: LexisNexis Butterworths, 2005)
Bibliographic record
Abstract
It is now trite to say that the events of September 11, 2001 have had a profound impact on our national security, in terms of its institutional and normative dimensions, and also in terms of a more general public anxiety. The hastily enacted Anti-terrorism Act of 2001 brought about significant changes to a wide range of statutes including, among others, the Criminal Code, the Official Secrets Act, the Canada Evidence Act, and the Proceeds of Crime (Money Laundering) Act. An early conference and resultant book on the Anti-terrorism Act raised serious concerns about the potential impact of the changes on civil liberties. However, for the most part civil libertarian concerns were diluted by more widespread fears for personal and national security, and perhaps also by the sense that law abiding citizens would not, in any event, be affected. Several high profile cases and the Maher Arar Inquiry and Report have since drawn attention to both the civil liberties concerns, and the potential impact on ordinary citizens of intelligence-gathering and intelligence-sharing activities. In this context, Stanley Cohen’s detailed and comprehensive book examining the privacy and security regime in Canada makes a very important contribution to the liter- ature in this area. It lays a thoughtful and balanced foun- dation for ongoing debate over issues at the intersection of privacy, crime, and terror.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".