Bibliographic record
Abstract
Multi-user efficacy is a key factor of genuine collaboration among multiple users towards a common goal. To assess multi-user efficacy, social scientists have traditionally applied subjective measurements from a theoretical perspective. Researchers in human-computer interaction have developed combined metrics of objective and subjective measurements. Nevertheless, the combined metrics fall short to fully cover the theoretical perspective of social scientists. To remedy this shortfall, we have developed a set of objective and subjective metrics to complete the theoretical perspective. Utilizing the metrics, we present in this paper a study to verify the robustness of our dynamic priority (DP) model, which under a quasi-practical scenario resolves command conflicts and promotes perceived equality in interaction among multiple users. In the study, we utilized a realistic scenario which differs from the quasi-practical scenario in the allowance of verbal communication among users. The results of the study revealed that the DP model yielded a significantly higher degree of multi-user efficacy under the realistic scenario than the quasi-practical scenario. Moreover, there was no significant difference of the perceived equality in interaction between both scenarios. These observations confirm the robustness of the DP model, and imply the potential application of the model for genuine collaboration within multi-user VEs.
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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 | 0.006 | 0.028 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".