A Comparison of Recruitment Methods for Drone Public Perception Surveys
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-3403.vid Public perception surveys report that remotely piloted aircraft systems (RPAS), or drones, continue to become more mainstream than ever before. However, the quality of these surveys varies due to biases in participant recruitment and survey methodology. This study presents a comparison of data sets that were acquired by a convenience recruiting strategy performed by the research team (n=233) and an online market research panel performed by Qualtrics (n=1022). Results show that the contracted recruitment yielded a sample that closely resembles the requested socio-demographic quotas, while the researcher-recruited data was skewed toward young adults. Furthermore, results from questions pertaining to general attitude towards RPAS and mission-based support for RPAS differed for the two data sets, with the higher level of support generally belonging to the researcher-recruited group. Finally, a set of best practices is proposed for participant recruitment and data collection to better standardize drone public perception surveys.
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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 | 0.301 | 0.343 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".