Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Research indicates that the two main causes of being overweight and obese are living a sedentary lifestyle and unhealthy eating habits. Influencing people to be active and exercise is an active research area that has resulted in the development of several games both commercially available and for free. The area of influencing people to develop healthy eating habits, on the other hand, still has room for growth. In the current paper, I review existing serious games for healthy nutrition over the past five years and summarize the main findings based on three main themes: the design and development of the game, the evaluation of the game, and the findings from the evaluation. My results indicate that most games are designed in collaboration with a team of experts such as nutritionists, psychologists, HCI designers, and software developers. In addition, most of the games for kids are web-based while most of those for adults are mobile-based. Most games used a self-report approach to evaluation which was carried out over a range of period of 30 minutes to 90 days with between 10 to 531 participants. There were mixed results from the evaluations with most games partially achieving their aim. I conclude by suggesting guidelines for developing serious games for influencing healthy nutrition.
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.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it