Understanding the design space of referencing in collaborative augmented reality environments
Bibliographic record
Abstract
For collaborative environments to be successful, it is critical that participants have the ability to generate effective references. Given the heterogeneity of the objects and the myriad of possible scenarios for collaborative augmented reality environments, generating meaningful references within them can be difficult. Participants in co-located physical spaces benefit from non-verbal communication, such as eye gaze, pointing and body movement; however, when geographically separated, this form of communication must be synthesized using computer-mediated techniques. We have conducted an exploratory study using a collaborative building task of constructing both physical and virtual models to better understand inter-referential awareness -- or the ability for one participant to refer to a set of objects, and for that reference to be understood. Our contributions are not necessarily in presenting novel techniques, but in narrowing the design space for referencing in collaborative augmented reality. This study suggests collaborative reference preferences are heavily dependent on the context of the workspace.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".