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
Deictic reference -- pointing at things during conversation -- is ubiquitous in human communication, and should also be an important tool in distributed collaborative virtual environments (CVEs). Pointing gestures can be complex and subtle, however, and pointing is much more difficult in the virtual world. In order to improve the richness of interaction in CVEs, it is important to provide better support for pointing and deictic reference, and a first step in this support is to determine how well people can interpret the direction that another person is pointing. To investigate this question, we carried out two studies. The first identified several ways that people point towards distant targets, and established that not all pointing requires high accuracy. This suggested that natural CVE pointing could potentially be successful; but no knowledge is available about whether even moderate accuracy is possible in CVEs. Therefore, our second study looked more closely at how accurately people can produce and interpret the direction of pointing gestures in CVEs. We found that although people are more accurate in the real world, the differences are smaller than expected; our results show that deixis can be successful in CVEs for many pointing situations, and provide a foundation for more comprehensive support of deictic pointing.
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