Conflict resolution models on usefulness within multi-user collaborative virtual environments
Bibliographic record
Abstract
Conflict resolution models play key roles in coordinating simultaneous interactions in multi-user collaborative virtual environments (VEs). Currently, conflict resolution models are first-come-first-serve (FCFS) and dynamic priority (DP). Known to be unfair, the FCFS model grants all interaction opportunities to the agilest user. Instead, the DP model permits all users the perception of equality in interaction. Nevertheless, it remains unclear whether the perception of equality in interaction could impact the usefulness of multi-user collaborative VEs. Thus, this present work compared the FCFS and DP models for underlying the usefulness of multi-user collaborative VEs. This comparison was undertaken based on a metrics of usefulness (i.e., task focus, decision time, and consensus), which we defined according to the ISO/IEC 205010:2011 standard. This definition remedied the current metrics of usefulness that measures actually effectiveness and efficiency of target technologies, instead of their usefulness. On our multi-user collaborative VE, we observed that the DP model yielded significantly lower decision time and higher consensus than the FCFS model. There was, however, no significant difference of task focus between both models. These observations imply a potential to improve multi-user collaborative 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 | 0.013 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".