Location Tracking by Police: The Regulation of ‘Tireless and Absolute Surveillance’
Bibliographic record
Abstract
Location information reveals people’s whereabouts, but can also tell much about their habits, preferences, and, ultimately, much of their private lives. Current surveillance technologies used in criminal investigation include many techniques to track someone’s movements; not all are equally intrusive. This raises the following questions: how do jurisdictions draw boundaries between lesser and more serious privacy intrusions? What factors play a role? How are geolocational privacy interests framed? In this Article, we answer these questions through a comparative analysis of location-tracking regulation in eight jurisdictions: Canada, Czechia, Germany, Italy, the Netherlands, Poland, the United Kingdom, and the United States.\nWe analyze the legal status of location tracking through human observation, GPS tracking, cell-phone tracking, IMSI catchers (Stingrays), silent SMS, automated license-plate recognition, and directional Wi-Fi tracking in these countries. This results in highly context-dependent and case-specific assessments, in which eight factors play a role: use of a technical device, place, intensity, duration, degree of suspicion, object of tracking, covertness, and active generation of data. At a deeper level of analysis, we identify different conceptualizations of privacy underlying these assessments: not only classic privacy frames, such as communications secrecy, protection of home and body, and informational privacy, but also two new privacy frames: freedom of movement in combination with anonymity, and the mosaic theory. Thus, we discern a tentative but unmistakable shift in how lawmakers and courts assess the intrusiveness of location tracking, particularly of people’s movements in public space.\nTraditional privacy frames tend to downplay the seriousness of the privacy infringement enabled by location tracking, and our analysis demonstrates an increasing discomfort with this tendency, leading to the emergence of novel privacy frames (or theories) to regulate what might easily turn into what the Supreme Court of the United States has called “tireless and absolute surveillance.” We conclude that legal privacy frameworks developed in past centuries prove ill-suited for assessing the privacy-intrusiveness of contemporary location-tracking investigation methods, and that emerging, novel frameworks for understanding and protecting privacy may provide lawmakers and courts with the tools needed to address the challenge of preserving (geolocational) privacy in the twenty-first century.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".