Bibliographic record
Abstract
Lawful access—the legal regime that authorizes various methods used by law enforcement to intercept, search, or seize information for investigatory purposes—has been subject to much debate in Canada. However, those debates need a new solution space for the digital age. This must be able to incorporate new technological solutions for minimizing rights infringements and provide new forms of accountability and safeguards against misuse. This is not simply a matter of adopting the popular framework of “privacy by design,” or even a reworked “lawful access by design.” We argue that an appreciation of the challenges of the digital world requires us to rethink our basic constitutional framework. The Canadian constitutional framework for lawful access was set out by the Supreme Court of Canada in Hunter v Southam and was then refined in the subsequent jurisprudence. We argue that this framework is ill-suited to contemporary digital challenges to informational privacy and requires four fundamental shifts. First, this framework’s basic point-of-collection focus needs to shift to the broader life cycle of the data. Second, this framework needs to shift away from its categorical approach to informational privacy, where some categories of information are thought to be inherently more private than others, and instead approach informational privacy in terms of the use-context of the data. Third, this framework needs to shift away from its exclusive focus on procedural safeguards at the point of collection (e.g., the warrant requirement), and consider procedural, legislative, and technical safeguards throughout the life cycle of the data as well. Overall, this framework needs to shift away from a dominant focus on privacy and recognize a broader set of rights and interests at stake in lawful access practices, including the rule of law, equality, and other fundamental freedoms. Once we have a constitutional framework that is better able to address the digital era, then we can more precisely craft new techniques for protecting rights, ensuring accountability, and safeguarding against abuse within this framework. Indeed, we can then see why some of these techniques are constitutional requirements. The payoff for doing so, we argue, is a way of enabling specific justified uses of data by law enforcement, while safeguarding the data against non-justified uses over its life span.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".