Audio Description in Video Games Research in Progress
Bibliographic record
Abstract
As video games continue to grow in popularity, accessibility is a key concern which developers must consider to ensure the most people possible can enjoy the games they create (Cairns et al., 2019; Nova et al., 2021). With approximately 500,000 blind and partially sighted people in Canada alone, visual accessibility is a central concern of game accessibility. Visual accessibility has developed for decades with one of the most popular and effective methods of this being audio description (AD) (Fryer, 2016). Audio description comes in different styles depending on its use, with standard and extended AD being 2 of the most common types (Canadian National Institute for the Blind, 2019). Despite the success of this option in film and television, AD has not caught on in the game industry (SightlessKombat, 2020). This research looks to investigate AD as a method for visual accessibility in video games with a focus on determining the advantages and disadvantages of both standard and extended AD in this medium.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".