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
People using wheelchairs have access to fewer sports and other physically stimulating leisure activities than nondisabled persons, and often lead sedentary lifestyles that negatively influence their health. While motion-based video games have demonstrated great potential of encouraging physical activity among nondisabled players, the accessibility of motion-based games is limited for persons with mobility disabilities, thus also limiting access to the potential health benefits of playing these games. In our work, we address this issue through the design of wheelchair-accessible motion-based game controls. We present KINECT Wheels , a toolkit designed to integrate wheelchair movements into motion-based games. Building on the toolkit, we developed Cupcake Heaven, a wheelchair-based video game designed for older adults using wheelchairs, and we created Wheelchair Revolution, a motion-based dance game that is accessible to both persons using wheelchairs and nondisabled players. Evaluation results show that KINECT Wheels can be applied to make motion-based games wheelchair-accessible, and that wheelchair-based games engage broad audiences in physically stimulating play. Through the application of the wheelchair as an enabling technology in games, our work has the potential of encouraging players of all ages to develop a positive relationship with their wheelchair.
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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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