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
This research aims to investigate if using a more embodied interface that includes motion cueing can facilitate spatial updating compared to a more traditional non-embodied interface. The ultimate goal is to create a simple, elegant, and effective self-motion control interface. Using a pointing task, we quantify spatial updating in terms of mean pointing error to determine how two modes of locomotion compare: user powered motion cueing (use your body to swivel and tilt a joystick-like interface) and no-motion cueing (traditional joystick). Because the user-powered chair is a more embodied interface providing some minimal motion cueing, we hypothesized it should more effectively support spatial updating and, thus, increase task performance. Results showed, however, the user-powered chair did not significantly improve mean pointing performance in a virtual spatial orientation task (i.e., knowing where users are looking in the VE). Exit interviews revealed the control mechanism for the user-powered chair was not as accurate or easy to use as the joystick, although many felt more immersed. We discuss how user feedback can guide the design of more effective user-powered motion cueing to overcome usability issues and realize benefits of motion cueing.
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 | 0.000 | 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 | 0.001 |
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