Trashcans and Constitutional Custodians: The Liminal Spaces of Privacy in the Wake of Patrick
Bibliographic record
Abstract
In the shadow of the recent Supreme Court decision of R. v. Patrick (2009), this paper discusses the Court’s development of the threshold issue in s.8 Charter protections from unreasonable search and seizure – the reasonable expectation of privacy analysis. I discuss Patrick as a capstone in a canon of recent reasonable expectation of privacy decisions. Patrick demonstrates a Court more determined to contextualize and objectively analyze privacy expectations but also a Court more likely to see privacy as informational. Concomitantly, these judicial predilections open up the expectation of privacy analysis to concerns of law enforcement efficiency. The implications may profoundly affect the way courts analyze access to s.8 protections. More surprisingly, the privacy canon may demonstrate a shift in the Court’s constitutional role to a more dilute form of constitutional guardianship, apprised of more contextual concerns, and in a relative sense, rooted in the efficacy of crime control rationales.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.045 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 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".