Humansystems ® Incorporated Approved by
Bibliographic record
Abstract
2006 Humansystems ® Team Modelling: Literature Review Page i Increasingly, Canadian Forces operations require the use of highly complex teams that function in joint, interagency, and often distributed environments. This report is a literature review of scientific and military research pertaining to team performance. This review consists of three sections. First, the factors influencing team performance are explored, and three major sets of factors are considered in relation to team performance: team factors, task factors, and team processes. Although very large, the team performance research has generally not built progressively upon previous work and illustrates equivocal results. Nonetheless, it is clear that characteristics of the team, the task, and team processes are all important influences on team performance. However, exactly how each of these factors influences team performance is often dependent on other factors. Moreover, the majority of the existing team research is limited in that it has not generally been conducted in realistic settings. The second section addresses measures of team performance, considers
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.757 | 0.602 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".