Bibliographic record
Abstract
Games use the same base technology and design strategy as do simulations, but add a few items to the mixture. Understanding this gives ‘new’ (read borrowed) tools for game creation and testing. The idea that simulations are implementations of a model, for instance, leads to a focus on the model rather than the code when designing a game. Similarly, the verification/validation pair used in simulations can be extended by adding playtesting for games, thus giving an educational game (for example) viable, demonstrable educational characteristics as well as playable (and thus engaging and motivating) characteristics. Productive work on improving games for specific purposes (serious games) can be advanced if the authors can agree on a common terminology and concept set (Shaw & Gaines, 1989), and if games can be seen as a valuable extension of a simulation that has specific characteristics that make them useful in specific circumstances. The idea of ‘fun’ is often thought of as the enemy of ‘learning’ in educational literature, and this needs to change if progress on serious and educational games is to be made. This paper will describe the hierarchy of computer simulation objects within which ludic simulations can be understood.
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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 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".