Bibliographic record
Abstract
Defence Research and Development Canada (DRDC) Toronto is in the process of developing team research scenarios aimed at supporting the Canadian Forces (CF) future integrated operations, and interoperability with allies, other government departments (OGDs) and non-government organizations (NGOs). This work falls within a 4-year Applied Research Project (ARP) to include a literature review of relevant team literature, the creation of a platform for conducting experiments on teams, the running of team experiments using a scenario involving one or more Human Systems Integration (HSI) intervention(s), the development of a computational model of team performance, and some preliminary validation of this model. Previous reports (Sartori, Waldherr and Adams, 2006; Go, Bos and Lamoureux, 2006) have reported the outcomes of exhaustive literature reviews on team research and team research platforms respectively. This report describes the outcomes of two parallel streams of work. The first stream was the development of three team experimental scenarios, in a domestic operational context, appropriate for studying the targeted teamwork factors (i.e. teams-of-teams, joint, interagency, distributed environment). This was done by identifying and reviewing scenarios used previously in team
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".