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Record W1485347139

Passing the Sniff Test: Police Dogs as Surveillance Technology

2012· article· en· W1485347139 on OpenAlexaboutno aff
Irus Braverman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsnot available
Fundersnot available
KeywordsScrutinyConstitutionalitySupreme courtPolitical scienceOfficerLawPsychology
DOInot available

Abstract

fetched live from OpenAlex

In October 2012, the Supreme Court of the United States will review the case of Florida v. Jardines, which revolves around the constitutionality of police canine Franky’s sniff outside a private residence. Essentially, the Court will need to decide whether or not the sniff constitutes a “search” for Fourth Amendment purposes. This Article presents a review of the often-contradictory case law that exists on this question to suggest that underlying the various cases is the Courts’ assumption of a juxtaposed relationship between nature and technology. Where dog sniffs are perceived as a technology, the courts have been inclined to also define them as “searches,” thereby triggering Fourth Amendment protections. Conversely, when perceived as extensions of the officer’s natural sense of smell, dogs, like nature, are viewed with “superstitious awe” and spared constitutional scrutiny.\nRather than use the dominant judicial classification of police dogs as either “natural entities” or “advancing technologies” — each of which triggering its own, usually opposite, chain of legal events — I rely on the scholarship of Science and Technology Studies (STS) to suggest treating police dogs as “biotechnologies”: co-produced human-animal hybrids. I argue that although a dog seems to have limited development capacity in comparison to a nonorganic machine, the police dog’s various breeding, improved training, increased application, and machine augmentation render it both a biological entity and an advancing technology. I also argue that despite the common use of dogs as pets, a work dog — and a police detection dog in particular — is clearly not “in public use.” Specifically, the high cost of breeding, training, and maintaining K-9s, the professional training required, the unique human-animal relationship that develops in the highly volatile police setting, and the status of K-9s as full members of the police force — all demonstrate that K-9 Franky is not, and will probably never be, Spot or Rover. Finally, I claim that such novel categorization of police dogs as both a “bio” and a “technology” should at least trigger the same constitutional protections as an infrared device. Under no circumstances should any technology go a-priori unprotected by the Fourth Amendment, even when such technology is an eight-year-old chocolate Labrador retriever named Franky.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.650
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.023
GPT teacher head0.341
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2012
Admission routes1
Has abstractyes

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