The Social Accomplishment of Seeing Together in Networked Team Play
Why this work is in the frame
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Bibliographic record
Abstract
Background This article focuses on communication in team-based esports, particularly in the ways that callouts enable players in team-based First-Person Shooters (FPS) to collaboratively link their own perception and awareness of in-game actions to that of their teammates. Callouts are short, community-based utterances that players use to communicate vital details of fast-paced action in competitive games. Aim We provide an empirically-based theorization of why callouts appear to be especially important in team-based FPS games, which, because of the limited fields of vision and split-second decision-making, require players to communicate what is happening to the others in the team as they navigate the game environment. Methods To describe this distributed perception, we borrow from studies on active military settings that term this seeing together as interperceptivity and employ ethnomethodology in our analysis of the minute details of players’ actions in the screen recordings as they extended their team’s collective perception and awareness of in-game activities and events. Results Through this paper, we contribute to the ongoing research on understanding communication and collaboration in team-based games. The callout sequences (and aligning actions) are orienting towards sharing individual perceptions for the (co)construction of an interperceptivity of in-game activities. Hence, callouts form a precondition for coordinated play. Conclusion The introduction of this concept to game studies can help in making sense of a key capability in networked team-based games; that is, how players collectively construct a situational awareness that encompasses teammates’ perception. Also, because of the essential role of callouts and interperceptivity in highly-skilled networked play, we point to some of the cultural contexts in which this practice is accomplished.
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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 | 0.001 | 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 | 0.000 |
| 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