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Record W1907101914 · doi:10.1027/2192-0923/a000041

Understanding Variance in Pilot Performance Ratings

2013· article· en· W1907101914 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.

Bibliographic record

VenueAviation Psychology and Applied Human Factors · 2013
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsApplied psychologyPsychologyStatisticsVariance (accounting)Rating scaleReliability (semiconductor)Mathematics

Abstract

fetched live from OpenAlex

Two studies were designed to investigate how pilots of different rank evaluate flight-deck performance. In each study, the pilots were asked to assess sets of three different videotaped scenarios featuring pilots in a simulator exhibiting poor, average, and good performance. Study 1, which included 92 airline pilots of differing rank, was aimed at comparing how individuals rate performance. The subjects used a standardized assessment form, which included six criteria, each having a 5-point rating scale. Analysis of the first study revealed that there was considerable variance in the performance ratings between flight examiners, captains, and first officers. The second study was designed to better understand the variance. Eighteen pilots (six flight examiners, six captains, and six first officers) working in pairs evaluated performances, in a modified think-aloud protocol. The results showed that there were good reasons for the observed variances. The results are discussed in relation to inter-rater reliability.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.755
Threshold uncertainty score0.999

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)0.0100.001

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 head0.117
GPT teacher head0.358
Teacher spread0.241 · 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