Text and Context: Making Sense of Canada's New Personal Information Protection Legislation
Bibliographic record
Abstract
The normative provisions relating to privacy in Canada's new Personal Information Protection and Electronic Documents Act are as general as one might expect, given their origin in a voluntary, multi-sectoral model Code. The generality which is the virtue of a model code, however, may well be a vice for mandatory and enforceable legislation. This paper provides a critique of the provisions of the legislation that deal with the key concept of consent. The author argues that the problems with the legislation will make it particularly difficult for businesses and consumers alike to determine what privacy protections are required across a range of different circumstances. The author then explores a variety of tools which may be of use in determining what measures are required in different circumstances in order to comply with the legislation. These include the office of the Privacy Commissioner, past practice, other legislation, sectoral codes, privacy seal programs, P3P and privacy statement generators.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.030 | 0.035 |
| Scholarly communication | 0.027 | 0.010 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".