Bibliographic record
Abstract
Online board-game sites are popular settings for group activity. However, unlike many kinds of group interaction, previous research has found that there is often little verbal conversation during games, which seems strange in a social situation. One reason that has been suggested for the lack of talk is that actions in the game are themselves communicative acts that can replace verbal utterances. There is little known, however, about how games can substitute for verbal conversation. In this paper we carry out a study exploring meaning and shared understanding through the moves of a board game. Participants played a board game and then retrospectively analysed the communicative meaning of each move; we analysed their responses as indicative of "joint actions" in four layers of interaction. Our study shows that game moves can provide a great deal of communication, and that there are both similarities and differences to verbal conversations at each level. The physical layer, containing the board, is a foundation similar to that of people speaking the same language. The syntactic layer, consisting of the game rules, allows demonstration of expertise but is not noticed by players except in unusual circumstances. The strategic layer, consisting of competitive use of the syntactic rules, diverges considerably from a verbal conversation in terms of shared understanding, because players are actively attempting to avoid revealing their meaning. The personality layer allows players to make inferences about the other player as a person, just as in verbal communication. Our analysis provides new evidence that even simple turn-based games contain a great deal of interaction richness and subtlety, and that the different levels of communication should be considered by designers as a real and legitimate vehicle for social interaction.
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.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".