Breaking the Fourth Wall? User-Generated Sonic Content in Virtual Worlds
Bibliographic record
Abstract
Abstract The fourth wall is a term borrowed from dramatic theory that considers the theatrical stage as having three walls (two sides and a rear) and an invisible fourth-wall boundary between the actors and audience. This chapter considers the experience of user-generated sonic content in virtual worlds in terms of the concept of the fourth wall, situating this content in regards to the dynamic between audience and virtual space. While much of the work on user-generated content in virtual worlds has focused on this relationship between developers and players, there are many interesting aspects of user-generated content that have been neglected, particularly when it comes to sound. The chapter argues that user-generated sound is in a unique position with regards to breaking the fourth wall, presenting an overview of user-generated content in virtual worlds, and exploring how user-generated content contributes to the social interactions that occur and to the breakdown of the fourth wall. The chapter then focuses on the types of auditory content that are generated and shared between players, situating the use of sound as a mediator between the virtual and the real world spaces.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".