Reduction to Absurdity: Reasonable Expectations of Privacy and the Need for Digital Enlightenment
Bibliographic record
Abstract
: This article seeks a deeper understanding of privacy in the digital age through an examination of a phenomenon the authors call “information emanation”. Focusing on Canadian jurisprudence involving heat and odour emanations, the authors examine the current approaches of Canadian courts in decisions about the ‘reasonable expectation of privacy’. The authors focus on three judicial trends that pose serious risks to privacy: 1) the tendency to equate different kinds of emanations and conclude that information emanations into public spaces never attract a reasonable expectation of privacy; 2) a reductionist approach to information privacy, which obscures the deep social significance of police investigative techniques; and 3) the adoption of a non-normative approach to ‘reasonable expectations’ ushering in a shift in privacy discourse away from democracy, rights and duties towards an inquiry about digital technology and standards of police practice. The authors conclude that while the Supreme Court of Canada attempted to guard against many of these risks, recent jurisprudence indicates an ongoing threat of backslide to the reductionist approach to informational privacy, especially in future cases involving emerging digital technologies.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.050 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".