Arch play: applications of narrative theory in video game aesthetics
Bibliographic record
Abstract
As of 2015, the incumbent international eSports paradigm centers on genre-defining systems and games that were not initially designed for mass spectatorship. As a result, would-be fans are often confronted with a high-friction onboarding process verging on hostility. With global viewership estimated to reach over 238m unique annual viewers by 2017 (Superdata, 2015), leading developers have adapted the designs of new products to prioritize audiences as well as players. The most successful among them have capitalized off of the resulting spectator virality. Lacking is a high-level framework for evaluating games based on aesthetic composition and their resulting viability as a spectator experience. This paper offers critical evaluations of dominant and lesser-known gaming spectator experiences via in-depth analyses of their constituent design affordances relating to a combined, interdisciplinary aesthetic framework centered heavily around narrative-bias. It is asserted throughout that any viewing experience with certain aesthetic factors configured to prioritize a clear and approachable classical narrative design, when evaluated aesthetically, can be considered rich in quality. Conforming to this aesthetic standard also permits games the potential to enjoy mass popularity. This paper is intended to serve as a foundation for an interdisciplinary framework of best practices in video game design.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".