Gameplay Phenomenologies: Hermeneutics and the Exploration of Player Experiences
Bibliographic record
Abstract
This paper considers the work of Barthes and Gadamer in the analysis of player practices. Much of the study of player agency has been relegated to the subcategory of player studies, with very little conversation between this area of study and the areas of rhetoric and philosophy. Moreover, apart from Bogost’s Procedural Rhetoric , the various ways that games can function rhetorically is still under-theorized within game studies. This work attempts to create a dialogue between player studies and hermeneutic philosophy, focusing on how games can engage players hermeneutically and provide spaces to generate various narratives. Taking a cue from Barthes, I consider how games can function as writerly texts, generating emotion through ambience. Using Gadamer’s work, I illustrate how games create experiences, and how some players utilize hermeneutics in the ways by which they critically engage with the virtual worlds they occupy. Games can create aesthetic experiences, and players can be transformed in the act of play.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".