The Emergence of High-Speed Interaction and Coordination in a (Formerly) Turn-based Groupware Game
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it