Support of collaborative work in battlespace management: Shared (loss) of situation awareness
Bibliographic record
Abstract
In this paper, we report results from a large-scale military experiment with Canadian Forces (CF) officers on the impact of collaborative work support systems-in the present experiment, a set of integrated tools such as operations planning systems, joint fire systems and other logistics tools-on individual and shared situation awareness (SA). In order to measure SA and the ability to share SA, we used the Quantitative Assessment of Situation Awareness (QUASA) technique: We first analyzed SA quality (sensitivity, response bias, accuracy) and metacognition (level of confidence, and calibration bias), and then computed the level of response concordance within and across groups (three different Operational Commands of the CF). The addition of the new support system led to a significant improvement in shared SA. However, this beneficial effect comes with a drop in both objective and perceived individual SA. From this pattern of results, we conclude that there might be a tradeoff between SA sharedness and quality. Supporting the process of sharing SA through enhanced means of information integration and exchange, communication and coordination can also lead to a considerable decrease in individual SA and meta-SA.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".