Virtual Searches: Regulating the Covert World of Technological Policing
Bibliographic record
Abstract
Virtual Searches is a timely and well-written addition to the widespread debate on how surveillance technologies should be regulated and by whom. It explores police investigatory techniques carried out technologically rather than via physical intrusion, as well as how the Fourth Amendment and the regulatory landscape is dealing with many of these new police abilities. Potentially most interesting about Slobogin’s book is that it takes a different stance from some elements of the present thought surrounding topics such as predictive policing. It resists the typically prevailing abolitionist perspectives and instead putting forward, with evidence from case law and notable use case examples, the idea that there is a non-zero-sum game in which both government and the public can regulate these new technologies to gain their benefits with mitigations in place against potential drawbacks. The book concludes with a list of concrete suggestions for regulation of novel policing technologies in a way designed to harness their anti-crime power whilst protecting against many of the negative social and ethical repercussions surveillance brings with it. How effective these would be is likely to be the subject of fruitful future discussion in this area. In all, Virtual Searches is a great addition to ongoing police technology debates, likely to be of use to legal scholars from both the U.S. and abroad due to the universality of its themes and the clear way that U.S. legal concepts are explained and explored throughout.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".