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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.060 | 0.047 |
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 source (direct Gemma or distilled Codex), 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".