Bibliographic record
Abstract
OBJECTIVE: The current study developed an online hangar talk survey (HTS) to solicit narratives describing challenging scenarios that professional pilots encountered during the hours-building phase of their career. BACKGROUND: The predicted pilot shortage will effectively reduce the minimum flying hours required for pilots to be hired at an airline, resulting in less opportunity to develop nontechnical skills naturalistically. To compensate, threat and error data from the hours-building phase of a pilot's career are required to inform training development. Pilots often share stories of such experiences, colloquially termed "hangar talk". METHOD: The HTS gathered 132 narrative descriptions of general aviation (GA) events from pilots along with the event's impact and whether the pilots would react differently if the scenario were encountered again. RESULTS: The distribution of threats reported by GA pilots was similar to that reported at the airline level. Logistic regression analysis revealed that decision-making errors were associated with recognition of the need to react differently in the future, and decision-making errors and proficiency errors were associated with greater perceived impact on skill development. CONCLUSION: The current HTS solicited an array of data similar to the findings of airline-based threat and error observations. Pilots perceive decision-making and proficiency errors as impactful on skill development. APPLICATION: An HTS can be used to gather naturalistic threat and error data and to create a database of operational stories that can be used to develop nontechnical training based on narrative thought.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.012 |
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".