Bibliographic record
Abstract
This chapter covers the emerging area of the use of commercial off-the-shelf (COTS) computer games for military, defense and security purposes. A brief background is provided of the historic link between games and military simulation, together with the size and scope of the modern computer game industry. Considerable effort is dedicated to providing a representative sample of the various defense and security usages of COTS games. Examples of current usage are drawn from a range of nations including the United States (U.S.), Australia, Denmark, Singapore and Canada. Coverage is broken into the three chief application areas of training, experimentation and decision-support, with mention of other areas such as recruitment and education. The chapter highlights the benefits and risks of the use of COTS games for defense and security purposes, including cost, acceptance, immersion, fidelity, multi-player, accessibility and rapid technological advance. The chapter concludes with a discussion of challenges and key enablers to be achieved if COTS games are to obtain their true potential as tools for defense and security training, experimentation and decision-support. Aspects highlighted include the dichotomy between games for entertainment and “serious” applications; verification, validation and accreditation; collaboration between the games industry and defense; modifiability, interoperability; quantifying training transfer; and a range of technological challenges for the games themselves.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".