Agency as commitment to meaning: communicative competence in games
Bibliographic record
Abstract
Agency has long been considered one of the core pleasures of interacting with digital games. Recent treatments of agency in games culture and game design have grown increasingly concerned with providing the player with limitless freedom to act. While this describes one form of pleasure, in narratively focused games it has the unfortunate consequence of pitting the agency of the player against the will of the designer. We contend that for narrative games it is valuable to refocus our definitions of agency on the notion of meaning, and propose a treatment of agency that emphasises communicative commitments. This form of agency draws on ideas from speech act theory, and relies on a degree of ‘communicative competence’ on the part of both the game designer and player in order to function. We discuss mechanisms for training players in the necessary literacies needed to commit to meanings in games, and provide an example analysis of a game that successfully accomplishes this task.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".