Bibliographic record
Abstract
As military forces around the world begin to adopt gaming technology as an apparently cost effective and robust means for military tactical training it seems appropriate to consider how well suited they are for this task. This paper uses an evidence based approach to illustrate how American, British, Canadian and Australian forces are applying serious game (SG) technology to meet a variety of training needs. In particular, the paper uses these specific examples to address three questions: What tactical training requirements are serious games best suited to meeting? How effective and efficient are they at meeting those requirements? What are the technological limits associated with their use? In answering these questions, the paper concludes that SGs are providing a cost-effective means to provide experience-based learning with emphasis on cognitive and increasingly affective training domains. War fighters will not develop the expert psycho-motor skills they need to effectively employ their weapon systems using game-based training. However, once the team of experts in various weapon systems is created, SG technology affords trainers the opportunity to turn them into an expert team capable of communicating well with the cognitive skills they need to effectively operate as teams. The examples demonstrate that this is true for infantry, armoured or combined arms training in open or urban terrain and holds for the very technologically demanding case of aviation training. To take full advantage of this capability, SGs need to be included as part of blended training solutions that take advantage of the strengths of the various types of training available with the SGs providing an experience-based learning alternative that has not been practically affordable since the end of the Cold War. ABOUT THE AUTHORS
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".