Bibliographic record
Abstract
A comprehensive overview of how civilian drones sense the world and how they build the aesthetic imaginaries of our communities. Drone technology has garnered critical attention across many fields, from engineering to the humanities. While the first wave of drone scholarship was key in initiating the debate on drones, it also privileged the idea of the “scopic regime”—a militarized regime of hypervisuality—in its analyses of the connection between vision and power. The Sensorium of the Drone and Communities broadens the drone's spectrum of perception by acknowledging its creative, life-affirming possibility with the notion of the sensorium. The sensorium of the drone is a multimedia, synesthetic sensing assemblage in which the human agent is enmeshed with the drone. Drone sensoria can sense in many more ways than the scopic regime—with sound, touch, smell, temperature, and movement. In The Sensorium of the Drone and Communities, Kathrin Maurer shows how drone sensoria can change our understanding of human communities by constructing imaginaries of social communities based on decentralized and fluid sensing processes. Maurer takes an aesthetic approach to technology, working with two understandings of aesthetics. One understanding refers to aesthetics as a way of experiencing, and it explores how the drone-human assemblage perceives the world. The other refers to aesthetic mimetic representation, and focuses on how aesthetic drone imaginaries in literature, popular culture, visual arts, and films negotiate the sensorial technology of the drone. Bringing together key ideas in technology studies, studies of aerial views, visual and aesthetic studies, posthuman sensing, machine–human interaction, and communities, The Sensorium of the Drone and Communities sheds a welcome and necessary light on this technology's creative potential as well as its dangers and risks.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".