Constructive simulation versus serious games: a Canadian case study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As military forces around the world embrace modelling and simulation as a fundamental enabling technology necessary to help meet training requirements, the impressive characteristics of video game technology and the advent of serious games are increasingly becoming an important part of the training tool kit. The Canadian Army's Directorate of Land Synthetic Environments (DLSE) is charged, in part, with the conduct of command and staff training that is typically supported with a constructive simulation. In addition to simulating the battle, the simulation also stimulates the go-to-war command and control (C2) systems such that the headquarters staff (as the primary training audience) can be immersed in the tactical scenario by performing their usual battle procedures in a mock-up Command Post. After 11 years of conducting exercises in this manner, DLSE supported it's first serious game based exercise in October of 2006. Exercise Winged Warrior is the culminating activity at the end of the Advanced Tactical Aviation Course, intended to train pilots to perform as aviation mission commanders and air liaison officers. This paper takes a critical look at the similarities and differences between exercises primarily supported by constructive simulation versus those supported by a serious game. It also introduces the concept of a training needs framework upon which decisions regarding the most appropriate type of tool to support a training objective can be planed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it