Bibliographic record
Abstract
Privacy law, to the extent that it regulates state information practices, wears two “public” hats. The first hat is constitutional law. For example, the Canadian Charter protects privacy through protecting individuals against unreasonable searches and seizures. The second hat is public sector data protection law modelled on what are known as Fair Information Practices (FIPs). For example, in Canada the federal Privacy Act regulates the collection, use and disclosure of personal information held by government institutions and provides individuals with a right of access to that information. The constitutional hat is concerned with state-individual relations in the context of law enforcement while the data protection hat is concerned with state-individual relations in the context of administering state programs. This article calls into question the ongoing relevance of this divide. The merging of these two frameworks is a large project to both undertake and defend. This article only purports to offer some initial reflections on a potential merger, focusing on recent Supreme Court cases, including R. v. Spencer; R. v. Wakeling; and R. v. Fearon. First, this article outlines some of the ways in which our Charter jurisprudence already adopts some of the insights that come out of the data protection law model and points to some of the ways in which this can be built upon. Next, the article outlines the potential problems of using data protection law framework in the context of law enforcement and anti-terrorism if the limitations of data protection are not well understood when balancing interests. Finally, it finishes with some proposals about how merging the two models might better address some new types of “Big Data” investigatory techniques, or, what we now all describe post-Snowden, as “collecting-the-haystack-to-find-the-needle”.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".