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
Advances in human-computer interaction (HCI) technologies have led to emerging computer game systems that foster physical exertion as part of the interaction; we call them exertion games. These games highlight a body-centric perspective on our interactions with computers, in contrast to traditional mouse, keyboard and gamepad interactions, not just in terms of their physical interface, but also in terms of the experiences that they support. As a result, exertion games show great promise in facilitating not only health benefits, but also novel play experiences. However, to realize this promise, exertion games need to be well designed, not only in terms of technical aspects involving the sensing of the active body, but also in relation to the experiential perspective of an active human body. This article provides an overview of existing work on exertion games, outlines a spectrum of exertion games, and presents an analysis of key enabling technologies. We also position exertion games within a broader HCI context by reviewing and examining different design approaches and frameworks for building exertion games. Finally, the article concludes with directions for future work.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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 it