Identifying Latent Cognitive Constructs in a Comprehensive Model of Aviation Outcomes: The Role of the Dynamic Mental Model for Pilots
Bibliographic record
Abstract
I wish to acknowledge the pilots who volunteered their time and considerable talents flying the simulator.I learned so much about aviation, and about the strong culture of safety that exists within this community of pilots.In particular, I thank Simon Garrett for his many hours piloting our simulation protocols and providing guidance to this work.To the general aviation community in Ottawa, thank-you for encouraging me to share the results of this work with you in various venues.Every opportunity to speak was, in reality, a learning experience for me as well.Anne Barr and Andrew Staples, your expertise in designing the simulation environment and your willingness to support me in solving any technical mishap, at a moment's notice, kept me from losing my nerve and inspired me to keep scheduling participants!Anne and Andrew, over time I grew in appreciation of your skills, but also in appreciation of your friendship.And Anne-you let me fly-wow, what an experience!I also thank Jon Wade for your generosity of time and talent in assisting me with writing programs so that the myriad of data could be handled so efficiently.This helped me more than you might have realized.To my family and friends, thank-you is just not enough.Without your
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".