Bibliographic record
Abstract
The Consumer Privacy Protection Act is the first proposed privacy statute to regulate the deceptive privacy practices that undermine individuals’ right to consent. The problem is that there is no framework for determining how the Act might actually apply. This article resolves the issue by filling three gaps in the literature.First, it categorizes different types of deception according to privacy law’s notice-and-choice framework, providing a method of analysis for scholars and regulators. It then concretizes the framework by comparatively surveying investigations led by the United States’ Federal Trade Commission and Office of the Privacy Commissioner of Canada (OPC). This will shed light on how the Act can be interpreted, and will constitute a comprehensive survey of a thematic area of OPC investigations.Finally, the article explores whether the Act defines consent as an act of ongoing agency, which would protect peoples’ privacy by covering deception that occurs not only at “I agree moments,” but also beyond “I agree moments.” Ultimately, this article guides judges and regulators in enforcing the Act, assists policy-makers in developing more statutory provisions that regulate deceptive privacy practices, and contributes to doctrine by filling the aforementioned gaps.
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 | 0.048 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.111 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".