Drone Use for COVID‐19 Related Problems: Techno‐solutionism and its Societal Implications
Bibliographic record
Abstract
Drones have been widely used by public authorities during the COVID-19 pandemic for pandemic-related problems. As an innovative tool with a wide range of potentialities, they have been deemed suitable for an exceptional situation marked by the persistence of social distance. Yet, the turn to new technology to solve complex problems is a political decision that has broad societal implications, especially in the context of declared states of emergency. In the article we argue that the extensive use of drones by national authorities during the COVID-19 pandemic has generated a new socio-technical assemblage of actors, technologies and practices. Building on the three main uses of drones as responses to specific pandemic-related challenges (disinfection, delivery, and surveillance), we analyse the actors and the practices involved in this new socio-technical assemblage. From the empirical material, we explore potential effects of drone uses on key issues such as the technology regulatory processes, public acceptance, and security and safety concerns.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".