Public Opinion About the Benefit, Risk, and Acceptance of Aerial Manipulation Systems
Why this work is in the frame
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Bibliographic record
Abstract
Aerial manipulation systems are an emerging subclass of unmanned aerial vehicles (UAVs or “drones”) that use a mobile arm to manipulate their environment. Public opinion is an important consideration for drones, but past public opinion polls have focused on drones without attached arms and have relied on text-based surveys. Study 1 ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> = 190) assessed participants’ perceived benefit, risk, and acceptance of aerial manipulation systems across five applications, using an animation-based online public opinion survey. Study 2 ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> = 194) assessed the influence of stimulus sampling on public opinion of aerial manipulation systems by replicating Study 1 using an alternative set of animations. Study 3 ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> = 396) assessed the influence of using animations versus a text control on perceptions of the poll and aerial manipulation systems, as well as the influence of survey platform (YouGov Direct versus Mechanical Turk). Results show that delivery applications are perceived as more beneficial than several other applications; that people’s opinions and imagining of drones were different if they watched animations versus read text; and that stimulus and population sampling influence the results of public opinion polls about drones.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it