When the Profile Becomes the Population: Examining Privacy Governance and Road Traffic Surveillance in Canada and Australia
Bibliographic record
Abstract
Use of automated licence/number plate recognition (‘ALPR/ANPR’) technologies in Canada and Australia raises significant policy questions for privacy advocates and criminal justice practitioners. The proliferation of mass surveillance through ALPR/ANPR also presents several conceptual puzzles about the links among criminal justice data flows, individual privacy and state responsibility in this actuarial age. In this article, we use case studies of ALPR/ANPR in Canada and Australia to examine privacy as a technique for governing road traffic surveillance. We explain our findings in light of Harcourt's (2007) argument against the use of actuarial prediction and ‘hit rates’ that are rationalised as the chief measure of law enforcement activities and effectiveness. Finally, we question the regulation of surveillance technologies such as ALPR/ANPR through current Canadian and Australian information privacy laws, with specific focus on privacy by design (‘PbD’), a strategy that favours improving law enforcement efficiency at the expense of privacy.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".