The Personal Information Protection and Electronic Documents Act: A Comprehensive Guide by William Charnetski, Patrick Flaherty and Jeremy Robinson (Toronto Canada Law Book Inc., 2001)
Bibliographic record
Abstract
The Personal Information Protection and Electronic Documents Act: A Comprehensive Guide (the Guide) is the second book to be published in English in Canada dealing expressly with the Personal Information Protec- tion and Electronic Documents Act (PIPEDA). The Guide is different from the earlier work, in that it is not a section by section discussion or annotation of the provisions of PIPEDA. Rather, it is organized into eight chapters, each addressing a distinct theme or topic. The Guide is also aimed at a more professional audience than the earlier work. In its introduction, and in its choice of content, it focuses more on specific problems and interpretive issues under PIPEDA, than on giving a general overview and explanation of the legislation.\nThe authors of the Guide reflect the more profes- sional and practice oriented nature of the book. All three authors are lawyers whose areas of practice include varying aspects of privacy law. Their objective in preparing the work is clear; it is meant to identify legal issues of importance to lawyers and entrepreneurs. The brief introduction to the book refers to the legislation as ‘‘consumer protection for the digital economy’’, and cautions that: ‘‘no business or other entity to which the Act potentially applies can afford either to ignore it or to attempt to comply with its provisions in an ad hoc fashion’’.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.030 |
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".