Reviewing PIPEDA: Control, Privacy and the Limits of Fair Information Practices
Bibliographic record
Abstract
This article argues that the federal Personal Information Protection and Electronic Documents Act (PIPEDA) provides individuals with control over their personal information in order to protect informational privacy while permitting organizations to collect, use and disclose personal information for legitimate and reasonable purposes. However, in determining whether such control is effective in protecting privacy, a number of issues emerged as important: control over personal information can protect a broader set of values than simply privacy; individual informational privacy can be protected even in the absence of individual consent; determining the scope of the legal entitlement to control over personal information requires an understanding of the values that privacy is meant to protect and a balancing of these against legitimate claims of others in a principled manner; and control will only protect privacy if individuals are presented with meaningful choices regarding privacy options. These issues then helped to pinpoint a number of PIPEDA'S weaknesses, including its all-or-nothing approach to Schedule 1obligations; the scope of individual control over personal information provided and the role of implied consent; the desirability of an Ombudsman model; and whether PIPEDA'S provisions can require that privacy be taken into account at the stage of administrative and technological design.
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 | 0.041 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.037 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 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".