Investigating communication and social practices in real-time strategy games: are in-game tools sufficient to support the overall gaming experience?
Bibliographic record
Abstract
This paper discusses the social and strategic communication patterns observed during gameplay of the real-time strategy game, StarCraft II. An observational study was conducted over three weeks during which approximately 26 game matches and the social procedures by which players organized themselves and selected game options were observed. Study participants were members of a pre-existing network of friends and had adopted the Skype voice communication tool to support the game client's built-in collaboration and social networking solutions. The players were observed playing in situations of varying levels of collaboration ranging from team matches to free-for-all matches, and many forms of communication, including both strategic and social, were observed. The study findings revealed that players prefer communication tools that provide both robustness and flexibility. Preferred tools increase ease of access to other players, introduce a measure of exception handling to unify the gameplay experience, and make use of the game as a virtual watercooler---a hub which can facilitate much off-topic, yet valued, conversation.
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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 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".