Information Sharing and the 'Reasonable' Ambiguities of s.8 of the Charter
Bibliographic record
Abstract
One of the core issues regarding informational privacy in the 21st century is the issue of information sharing. The question is not whether the state will engage in information sharing practices, or even whether it should. The real question is whether such practices will be subject to constitutional scrutiny and thereby required to meet a rigorous standard of justification. The Canadian Supreme Court constitutional exhibits a number of key ambiguities regarding privacy that suggest courts will find it too easy to hold that such practices do not violate a reasonable expectation of privacy. First, the court shifts between a descriptive and a normative approach to defining privacy. Second, its descriptive approach often conflates the threshold question of defining privacy with the subsequent question of balancing privacy with other important social goals. Third, some of the more normative elements that do provide content to the reasonable expectation of privacy are norms of confidentiality, which, although related, are not the same thing as norms of privacy. This paper outlines these ambiguities and shows how they operate to discount privacy within the regulatory context but in a manner that is particularly problematic if applied without regard to the issues arising from information sharing.
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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 | 0.023 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| 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".