The Emergence of High-Speed Interaction and Coordination in a (Formerly) Turn-based Groupware Game
Bibliographic record
Abstract
Although some forms of distributed groupware now enable fast-paced real-time collaboration (e.g., first-person shooter games), little work has been done to determine how coordination and interaction occur when people attempt to work together at high speed. Understanding the elements of high-speed coordination is important, because shared-workspace groupware systems offer opportunities for new kinds of high-speed work that is, they provide freedom from the physical constraints that can slow and restrict coordination in physical shared spaces. To better understand high-speed coordination, and to examine whether these opportunities can enable new kinds of interaction in groupware, we created and studied a new multi-player game (called RTChess) that is based on traditional chess, but adds multiple players and removes all turns from the gameplay. The result is a free-for-all game where people are limited only by their ability to move quickly and expertly a situation that is more like a team sport than a tabletop game. We carried out an observational study of 448 games of RTChess to look for the emergence of high-speed interaction, team coordination, and interactional expertise. We found that people can interact extremely quickly through distributed groupware, and saw evidence that people build expertise and develop several kinds of coordination in the game. Groupware systems like RTChess indicate that coordination and interaction in shared-workspace collaboration can occur at high speed, and suggest ways to free groupware users from the slow and stilted interactions that are common in many current multi-user systems.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".