Public perception of UAS privacy concerns: a gender comparison
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While much research has examined engineering and practical uses of unmanned aerial systems (UAS), there have been very few studies that have examined privacy concerns that the public may have towards UASs. Even less research has been conducted on how gender and type of UAS mission may affect privacy concerns. This paper examines gender differences in privacy concerns across a wide array of UAS mission types. We also examine potential mediators that explain why females and males differ in their privacy concerns. A total of 1067 participants were presented with various hypothetical UAS missions across four studies. They were asked to provide privacy concerns scores and related information. The results of all four studies conclude that there are distinct gender differences in UAS privacy concerns. These differences are mediated by various factors. The researchers conclude that future UAS operation should take into consideration the public’s privacy concerns and that these concerns are different for females and males.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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