Bibliographic record
Abstract
Pointing gestures -- particularly deictic references -- are ubiquitous in face-to-face communication. However, deictic pointing can be much more difficult in collaborative virtual environments (CVEs) than in everyday life -- early studies found that the 'fragmentation' caused by the environment greatly complicated object-based communication. In the fifteen years since these studies appeared, the technologies used in CVEs have improved substantially, and several techniques for improving pointing have been proposed or implemented. What these advances mean for the problems of fragmentation and deictic gesture, however, is not clear. To find out, we conducted a new observational study of deictic pointing in a CVE with several techniques that may reduce fragmentation: extra-wide and third-person views, precise control over an avatar's pointing arm, and visual enhancements such as object highlighting and laser pointing. Our study shows that although pointing has come a long way, problems of fragmentation still occur, and that visual and view enhancements can cause new problems for collaboration, even as they solve others. In addition, the visibility of a gesture's preparatory actions remained important to study participants, even when pointing was augmented. These results provide a richer understanding of the subtlety in avatar-based deictic communication, and of the ways that this critical communication mechanism can be better supported in CVEs.
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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 | 0.003 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".