If the Supreme Court Were on Facebook: Evaluating the Reasonable Expectation of Privacy Test from a Social Perspective
Bibliographic record
Abstract
This article examines the Supreme Court of Canada's position that reasonable expectations of privacy in informational spaces can be protected by focusing on the protection of the information itself. It then measures this position against the findings of social science research studies that have examined the behaviour of young people in online spaces. The author argues that the legal test being advanced by the Court is out of step with what we know about people's online experiences and expectations. As such, the test may limit the Court's ability to protect us from surveillance technologies that negatively affect our dignity, autonomy, and social freedom. Especially as more of our public and private lives migrate to virtual spaces, it is essential that the courts begin to pay attention to the lessons to be gleaned from the social sciences research on privacy and reinvigorate the legal protection of privacy as a social value.
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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 | 0.071 | 0.274 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.029 | 0.037 |
| Scholarly communication | 0.023 | 0.010 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.016 | 0.017 |
| 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".