Constructive simulation versus serious games: a Canadian case study
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".