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Record W2785390672 · doi:10.1139/juvs-2017-0011

Public perception of UAS privacy concerns: a gender comparison

2018· article· en· W2785390672 on OpenAlex

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Unmanned Vehicle Systems · 2018
Typearticle
Languageen
FieldEngineering
TopicUAV Applications and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsPerceptionAffect (linguistics)Internet privacyInformation privacyPsychologyComputer securityComputer scienceCommunication

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.538
Threshold uncertainty score0.334

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.068
GPT teacher head0.283
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it