Bibliographic record
Abstract
This chapter proposes a new way of thinking about reasonable expectations of privacy, so that the legal norms formulated by the Supreme Court reach a better fit with the norms and expectations of Canadians. It proposes that privacy expectations should be based, first and foremost, on a full-fledged right to privacy, rather than just on section 8, or section 7, of the Charter of Rights and Freedoms. Second, our legal norms, particularly in the rapidly evolving area of privacy and technology, or digital privacy, should evolve so that they reflect societal norms over expectations of privacy. There are areas of law, such as tort law, where social customs can already serve as a normative source, and there are areas of law, such as criminal law, or human rights legislation (such as provincial human right codes) where the law prides itself on setting a normative standard that is higher, indeed often opposed to, the mores of society (such as they are). This chapter argues that privacy law should not be one of the latter areas. In fact, the protection of the right to privacy in our society, whether from other human beings, or from corporations, or from government, has been eroding at a greater and greater rate as technology continues to advance, and privacy has been offered less and less protection by private and public law. Privacy law is where the law falls short, and will fall even shorter, of society’s norms and expectations, if our legal norms are not brought to reflect the true expectations of Canadians. The argument advances in stages. First, the chapter outlines the current route in which courts arrive at and evaluate expectations of privacy in Canada. Then, the chapter summarizes some of the criticism already offered by scholars about our current case law and how it analyzes reasonable expectations. Further, the chapter builds upon this criticism to offer and suggest a different notion of privacy that should be recognized by the courts, in order to achieve the goals articulated above. Finally, the chapter concludes with some practical suggestions how to advocate for such expectations of privacy effectively.
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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 | 0.025 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.041 |
| Scholarly communication | 0.018 | 0.025 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".