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Record W2922971831 · doi:10.1027/2192-0923/a000153

Assessing Locus of Control in Pilots

2019· article· en· W2922971831 on OpenAlexaff
Hiten P. Dave, Karina Mesárošová, Alex B. Siegling, Paul F. Tremblay, Donald H. Saklofske

Bibliographic record

VenueAviation Psychology and Applied Human Factors · 2019
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsWestern University
Fundersnot available
KeywordsConfirmatory factor analysisLocus of controlInternal consistencyStructural equation modelingLatent variablePsychologyConstruct (python library)Construct validityConsistency (knowledge bases)AttributionSocial psychologyComputer scienceStatisticsPsychometricsMathematicsDevelopmental psychologyArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract. The present study systematically assessed the factor structure, internal consistency, and construct validity of the Aviation Safety Locus of Control scale (ASLOC) on 476 European pilots (4.6% female). Independent confirmatory factor analyses showed a strong correlation between the latent factors of Internal and External LOC, justifying proceeding with a one-factor solution (assessing internal LOC after reverse-scoring items). This model achieved adequate fit with excellent internal consistency, after refining with structural equation modeling. Furthermore, flight hours significantly predicted Internal LOC after controlling for age, suggesting that pilots’ work experience can enhance internal attributions of control. This has implications for safety-related behaviors and protection against accidents.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.100
GPT teacher head0.509
Teacher spread0.409 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2019
Admission routes1
Has abstractyes

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