Is there a ‘right to be forgotten’ in Canada’s Personal Information Protection and Electronic Documents Act (PIPEDA)?
Bibliographic record
Abstract
In this paper, I argue that PIPEDA could support a version of the right to be forgotten, subject to three important caveats. First, for search engines to meet the threshold applicability test under PIPEDA, their activities (i.e., crawling, indexing, organizing, etc.) must constitute the ‘‘collection, use or disclosure” of personal information. Ascribing such a role to search engines in information dissemination would likely require a court to distinguish the activities of search engines from hyperlinks on websites, which the Supreme Court in Crookes v. Newton determined did not involve control over content. Second, PIPEDA’s ‘‘all-or-nothing approach” means that if search engines met the threshold test, a series of obligations would be imposed on them regardless of their practicality, suitability or intelligibility. One of these obligations — to which exemptions are limited — would require search engines to obtain (and maintain) consent from individuals to collect, use or disclose their personal information. A court may react to the significant challenges of ‘‘fitting” PIPEDA to search engines by rejecting the application of PIPEDA at the threshold stage. Third, as the breadth of the right to be forgotten articulated in Google Spain would likely infringe the ‘‘core” of Canadian Charter protections for freedom of expression, one would expect any right recognized under PIPEDA to be far narrower than that under the Directive.\nThis paper proceeds as follows: Section 2 describes and contrasts the EU Directive and PIPEDA; Section 3 reviews and critically assesses Google Spain; Section 4 examines whether a right to be forgotten could be discovered in PIPEDA; Section 5 concludes.
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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 | 0.024 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.031 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.028 | 0.024 |
| Insufficient payload (model declined to judge) | 0.009 | 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".