An Overview of Cooking Video Games and Testing Considerations
Bibliographic record
Abstract
Video games are an important part of the lives of many people, being a popular entertainment medium, and providing engaging and motivating player experiences. Some types of video games are also played to learn about a particular topic, working as serious games (games that have a purpose beyond pure entertainment). That is the case of cooking games, a hybrid game genre where simulation, casual and other genres are involved. In cooking games, food preparation and presentation isare the central gameplay mechanic. In this paper, we present a review of popular cooking games, and suggestions for analyzing and testing cooking video games, taking into account the games' usability, training and learning components. These components could help designing and developing new cooking games. We conclude that in order to improve cooking games testing, testers should know the basics of cooking shown in the game beforehand, and be aware of the importance of the game's look and feel to evoke meaningful and compelling culinary experiences. Cooking games can help people to learn a life skill such as meal preparation, beyond just playing a casual game.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".