Bibliographic record
Abstract
This article provides a critique of military aerial drones being “repurposed” as domestic security technologies. Mapping this process in regards to domestic policing agencies in the United States, the case of police drones speaks directly to the importation of actual military and colonial architectures into the routine spaces of the “homeland”, disclosing insidious entwinements of war and police, metropole and colony, accumulation and securitization. The “boomeranging” of military UAVs is but one contemporary example how war power and police power have long been allied and it is the logic of security and the practice of pacification that animates both. The police drone is but one of the most nascent technologies that extends or reproduces the police’s own design on the pacification of territory. Therefore, we must be careful not to fetishize the domestic police drone by framing this development as emblematic of a radical break from traditional policing mandates – the case of police drones is interesting less because it speaks about the militarization of the police, which it certainly does, but more about the ways in which it accentuates the mutual mandates and joint rationalities of war abroad and policing at home. Finally, the paper considers how the animus of police drones is productive of a particular form of organized suspicion, namely, the manhunt. Here, the “unmanning” of police power extends the police capability to not only see or know its dominion, but to quite literally track, pursue, and ultimately capture human prey.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".