Protecting the Social Value of Privacy in the Context of State Investigations Using New Technologies
Bibliographic record
Abstract
In pursuit of security, governments around the world are adopting powerful technologies to collect and share detailed personal information, potentially leading to an erosion of privacy. This article discusses how legal analysis should respond to situations where technology developments challenge privacy interests in the context of state investigations. In particular, judges, lawyers and policy-makers need to take into more explicit account both the individual rights aspect of privacy as well as the social value of privacy, that is, society's interest in preserving privacy apart from a particular individual's interest. Both of these aspects of privacy are critical to the functioning of our democratic state. This approach demonstrates that legal analysis sometimes overstates the tension between privacy and security as both can be portrayed as social interests. To establish that a state search is constitutionally permissible under s. 8 of the Charter of Rights and Freedoms, the need to protect the social value of privacy compels the Crown to prove that a state agency has developed and initiated reasonable policies to govern the collection, use and disclosure of personal information through new surveillance 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 | 0.043 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.066 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.015 | 0.011 |
| 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".