Avoiding Friendly Fire: Constructing Behaviorally-Anchored Rating Scales to Assess Team Cognition in Distributed Mission Training for Close Air Support
Bibliographic record
Abstract
Distributed Mission Training (DMT), where trainees who are geographically distributed train for common missions using simulators connected via high-speed data networks, has been shown to be an effective training tool, particularly for Air Force operations. The Canadian Forces (CF) are interested in assessing the effectiveness and applicability of DMT for joint (air-land) and coalition missions. To this end, a rating instrument using behaviorally-anchored rating scales (BARS) was designed and trialed at Exercise Northern Goshawk, a distributed Close Air Support (CAS) simulation exercise that involved participants from the United States, the United Kingdom and Canada in August 2007. Effective team performance and training is essential in order to avoid friendly fire incidents while conducting CAS. The BARS instrument was thus designed to assess the quality of team coordination and performance at key moments of CAS missions. It is based on a validated Hierarchical Task Analysis of CAS missions as performed by Canadian Forward Air Controllers (FACs) and Pilots, and makes use of the behavioral markers of team cognition breakdowns proposed by Wilson, Salas, Priest and Andrews (2007). Here we describe the process of combining the behavioral markers and the HTA to develop the BARS. We also discuss the results that were obtained with the instrument, the challenges encountered while applying it, and propose future directions for validating and improving it.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".