Bibliographic record
Abstract
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".