Bibliographic record
Abstract
Debriefing is a major component of the job in many high-risk industries where errors can have considerable, often deadly consequences, including combat, surgery, and aviation. Although there exists considerable literature on debriefing, recent reviews of the literature suggest (a) shortcomings in the topics researched, (b) paucity of related theory, (c) limitations in the number of empirical studies, and (d) problems in research design. There are also recent suggestions that "there are surprisingly studies in the scholarly literature that show how to debrief, how to teach or learn to debrief, what methods of debriefing exists and how effective they are at achieving learning objectives and goals" Meta-analyses reveal substantial variations in research findings—e.g., on the use of video as a means of debriefing—that can be traced to the problems. This book redresses these problems in that it provides a detailed look at debriefing and assessment, the functions of different cognitive artifacts used, and a theoretical framework that accounts for the complexity of flying an aircraft and for the debriefing of the pilots’ experiences, especially under the high-stakes condition of their bi-annual evaluation for licensing purposes. The book provides detailed investigation of flight examiners’ methods to arrive at assessments of aviation pilot performance. It shows and theoretically models why there are good reasons for lower than desired inter-rater agreements. It offers detailed scenarios of how debriefing can be made to draw maximum benefit for pilot learning, that is, for the take-home messages that will make them better pilots. The theoretical framework includes objective factors that determine performance and the subjective experience pilots have while undergoing training and testing in flight simulators
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| 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".