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Airport security measures and their influence on enplanement intentions: Responses from leisure travelers attending a Canadian University

2014· article· en· W1964544944 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Air Transport Management · 2014
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversity of British ColumbiaYork UniversityUniversity of Manitoba
FundersUniversity of Manitoba
KeywordsAirport securityBusinessMarketingAdvertisingPsychologyComputer securityComputer science

Abstract

fetched live from OpenAlex

Airport security measures can be grouped into two types; standardized screening techniques, which all passengers must undergo (e.g., baggage X-rays, metal detecting scans); and elevated-risk screening (including pat-downs and strip searches) for which only a sub-set of passengers are selected. In the current study, an undergraduate sample (n = 636) was surveyed regarding the professionalism of security screening staff, as well as perceived safety, threat to dignity, and enplanement intentions, following standard and elevated-risk screening measures. Consistent with our hypotheses, perceived professionalism and safety were positively correlated with enplanement intentions, and dignity threat was negatively associated with perceived safety. As the perceived safety from the use of a security measure decreased, enplanement intentions also decreased. Notably, when a screening measure is perceived as having negative consequences (e.g., threatening one's sense of dignity) the safety of the measure is personally invalidated.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.040
GPT teacher head0.337
Teacher spread0.297 · 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