The Social Accomplishment of Seeing Together in Networked Team Play
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.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".