Bibliographic record
Abstract
Objective: To determine how flight examiners reason and which methods they use when assessing the competencies of pilots for continued accreditation purposes and type-rating training.Background: Early work on pilot performance assessment focused on measurement models, including the accuracy and reliability of the scores attributed to the human factors variables included. More recent studies investigated the nature of the evidence that flight examiners used. No previous studies were found on how flight examiners assess line pilots’ performance during flight training and examination.Method: This study employed methods typical for cognitive anthropology, combining ethnographic observations of debriefings and interviews, stimulated recall concerning debriefing, and modified think-aloud protocols of assessment of flight episodes. Twenty-three flight examiners from 5 regional airlines were observed and interviewed in 3 contexts.Results: The data revealed that flight examiners used the documentary method, where initial observations are treated as documentary evidence of underlying phenomena (e.g., situational awareness, decision making) while presupposing these phenomena for making and categorizing the observations. Flight examiners, using a variety of techniques, actively create situations for obtaining additional observations that further substantiate the presupposed underlying phenomena.Conclusion: Even when flight examiners use rating scales, their assessment method is based on categorization of facts and, therefore, shares similarities with medical diagnosis. Suggested quality improvement measures include increasing awareness of diagnostic error, developing diagnostic tools, and developing means to measure diagnostic errors.
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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 | 0.047 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".