Emanations, Snoop Dogs and Reasonable Expectation of Privacy
Bibliographic record
Abstract
Recognizing that valuable personal data emanates from our electronics, our personal effects, our homes and even our bodies, this article examines the notion of Focusing on current jurisprudence involving heat emanations from homes and odour emanations from knapsacks, the authors examine Canadian courts current approaches to information emanation in their decisions about the 'reasonable expectation of privacy' in the war against drugs. In anticipation of the Kang Brown and A.M. hearing at the Supreme Court of Canada, this article illuminates five main points. First, it contends that the majority of snoop dog decisions in Canadian courts have been wrongly decided; relying on an inappropriate use of judicial analogy that stems from a misreading of Tessling. Second, it warns against an excessively reductionist approach to informational privacy adopted in many recent reasonable expectation of privacy cases. Third, it warns against a non-normative approach to 'reasonable expectations' that is also gaining currency in several provincial courts across Canada. It suggests that the 'reasonable expectations' test has become a strange kind of factual inquiry. Fourth, the article proposes a different reading of Tessling, one that is better suited to the snoop dog cases and, perhaps more importantly, for subsequent application in cases concerning emerging high tech surveillance. Finally, it points to the future, suggesting that A.M. and Kang Brown are not just about snoop dogs; these two cases foreshadow the future of emanation information in a networked society. The authors conclude that the resolution of the snoop dog cases will not end the debate that started well over a decade ago. The authors argue that courts must confront the social implications of informational privacy much more deeply than they have, interrogating its meaning in an empirical universe of information emanation. The article suggests that a failure to clarify Tessling in the snoop dog cases and in the broader context of ubiquitous information emanation, especially alongside the maintenance of reductionist, non-normative approaches to informational privacy across Canadian courts, could seriously diminish the privacy rights of Canadians in a manner that the Supreme Court of Canada has until now been very careful to guard against.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.049 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".