From Wargaming to Peacegaming: Digital Simulations with Peacekeeper Roles Needed
Bibliographic record
Abstract
Militaries around the world have benefited from computerized games. Many recruits have been attracted to the military through military-style video games. After recruitment, games and simulations provide an important means of soldier training, including before actual deployments. However, electronic games are lacking for UN peace operations. The multidimensionality of peacekeeping has yet to be simulated in serious games to complement the many games that too often depict a binary battlefield of blue-team versus red-team (or, often in public games, good versus evil). Not only could soldiers benefit from nuanced and ambitious peace-related games, so too could civilian peacekeepers, and the public at large. Peacekeeping gaming should not be merely at the tactical level; the operational and strategic levels can be gamed as well. The decision-making in future peacekeeping simulations could help instruct conflict-resolution and critical thinking skills. The paper posits that such digital games could be an important tool for current and future peacekeepers, both military and civilian. Commercial games could also help educate the public on UN peacekeeping. The paper suggests that the United Nations partner with some member states and perhaps the video game industry to provide in-depth training simulations that mirror the challenges and complexities of modern peace operations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".