From horseback to the moon and back: Comparative limits on police searches of smartphones upon arrest
Bibliographic record
Abstract
The search of a smartphone by the police in connection with an arrest carries the potential to intrude into the very core of an arrestee’s private life. Indeed, such a search has been compared to providing a “window[] to our inner private lives,” including aspects of our lives completely disconnected from the reasons for the arrest. In recent years, the supreme courts of the United States, Canada, and the Netherlands (as well as Dutch legislators) have handed down rules about how, and whether, police may search an arrestee’s smartphone upon arrest without first obtaining a warrant or other court order. These responses can be categorized as either containerbased or content-based approaches, depending on whether the court (or legislature) focuses on protecting the privacy-sensitive content (for example, personal information) as such or, rather, the container (for example, the smartphone) as a proxy for protecting privacy-sensitive content contained within the device. After analyzing and comparing the approaches adopted in each of these three countries, we argue that both approaches have advantages and disadvantages, and we suggest a combination of the two as a fruitful path forward, balancing the important privacy and law enforcement interests at stake.
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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 | 0.015 | 0.149 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".