Proceedings of the Positive Gaming: Workshop on Gamification and Games for Wellbeing - Preface
Bibliographic record
Abstract
Gamification[5]and games have been used and studied in a variety of applications related to health and wellbeing [6,7,13]. Nevertheless, their application in the domains of wellbeing and flourishing[8,14](the pursuit of a happy and meaningful life rather than the simple in existence of illness) remain considerably less studied than other more common application areas, such as physical health or fitness. Therefore, this Workshop[15] aimed to provoke research and discussion by bringing together a community of interested researchers to discuss theoretical and practical considerations and promote the development of research projects focused on “Positive Gaming”as a technique for realizing the Positive Computing[2] objective of using technology to foster flourishing. A total of eight papers were accepted and presented at the Workshop. They addressed a rich variety of topics covering various areas of positive gaming including methods to understand users and design gameful applications for wellbeing. Examples application areas include motivating engagement in wellness activities, kindness interventions, nutritional interventions, and emotion regulation training; considerations for using technology to boost employee wellbeing; and opportunities for exploring game audio as a facilitator of wellbeing.
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 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) | 0.000 | 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".