“Jones-ing” for a Solution: Commercial Street Surveillance and Privacy Torts in Canada
Bibliographic record
Abstract
While street surveillance technologies such as Google Street View are deployed with no discriminatory intent, there is selective scrutiny applied to the published imagery by the anonymous crowd. Disproportionately directed at women and members of ethnic minority groups, this scrutiny means the social risks of street surveillance are not equal. This paper considers the possibility of invasion of privacy actions in tort brought against the commercial service provider as a possible solution. Analysis suggests that Canadian law has evolved in a way such that it is exceedingly difficult to make a claim for “privacy” in tort when the plaintiff is located in public space. This evolution exists in order to ensure that innocuous behavior not be rendered actionable. Furthermore, conceptual reasons exist to suggest that actions in tort are unlikely to be the best solution to the problems posed by commercial street surveillance. While any individual case of embarrassment or nuisance matters, broader “macro-harms” that impact entire communities reflect perhaps the most serious problem associated with the selective scrutiny of street surveillance imagery. Yet, it seems difficult to justify attaching liability for those harms to the commercial providers. While limits need to be placed on the operation of these street surveillance programmes, it is unlikely that invasion of privacy actions are the most effective way to achieve that goal.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.028 | 0.011 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".