Personality Measurement and Profile in a European Sample of Civil Airline Pilots
Bibliographic record
Abstract
Abstract. The study examined the psychometric properties of the Revised NEO Personality Inventory (NEO PI-R, UK edition) in a large European sample of civil airline pilots. The NEO PI-R is a comprehensive and robust measure of personality that has been validated across cultures and contexts. Furthermore, the personality profile of the pilot sample was examined and compared to a normative sample representing the UK working population. Data from 591 pilots (95.1% male) were collected. Analyses include the internal reliability and factorial validity (precisely, Exploratory Structural Equation Modeling) to examine the measurement equivalence of the NEO PI-R with reference to UK norms ( N = 1,301). Internal reliability estimates of the NEO PI-R scores were good at the domain level, but generally weak at the facet level. The structural model in the pilot sample was congruent with the general working population sample. Furthermore, there was convincing evidence for a distinct personality profile of civil pilots, although the stability of this profile will require further validation. The NEO PI-R’s validity in the assessment of general personality in civil airline pilots is discussed, along with implications of the results for the utility of personality assessment in civil aviation contexts.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".