Book Review: Privacy Law In Canada by Colin H.H. McNairn and Alexander K. Scott
Bibliographic record
Abstract
Privacy Law in Canada is a 360-page work that broadly covers legislation at both the federal and provin- cial level, and criminal and civil liability for privacy intru- sions in the context of case law from across Canada. Particular focus is given to privacy issues associated with the workplace, personal health information, technolog- ical surveillance, and protecting consumers and debtors. The authors take a practical approach in examining chal- lenging questions, such as whether a consumer’s consent is required to obtain a credit report; disclosure of med- ical information; monitoring an employee’s computer use and voice mail; how the PIPEDA affects businesses; the status of a common law tort of privacy; and when a person, who is subject to a search or surveillance, has a reasonable expectation of privacy. Informative, timely and straightforward, Privacy Law in Canada, is a very useful reference for practitioners and other professionals, as well as a good course supplement for law students or faculty studying, researching or interested in this area.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.016 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".