Domestic drones: the politics of verticality and the surveillance industrial complex
Bibliographic record
Abstract
Abstract. Drones are being introduced as innovative and cost-effective technologies for civil, commercial, and recreational purposes in the domestic realm. While the presence of these technologies is increasing, regulations are being introduced in order to ensure their safe and responsible use. As drones are adopted for a number of purposes, the “de facto practices settle around it, rendering change much more difficult” (Gersher, 2014), and so the policy debates must consider all contingencies and unintended consequences of their use. This paper discusses the background of unmanned aerial vehicles (UAVs), their role as surveillance technologies, and how they reinforce asymmetries in power and visibility that contribute to a politics of verticality, ultimately arguing that surveillance concerns must become part of the discussion at the policy and regulatory level in order to mitigate any harms. Where drones are already used for care and control as technologies of surveillance, privileged use of drones by public and police agencies could further reinforce a politics of verticality (Weizman, 2002), resulting in specific types of space, risk, and population management.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".