Accessible Play: Towards Designing a Framework for Customizable Accessibility in Games
Bibliographic record
Abstract
Video games are an important form of entertainment and have become an increasingly popular pastime in the 21st Century. However, many people with disabilities are still excluded from gaming due to accessibility barriers. While some progress has been made in recognizing accessibility as a design value, there is still a significant need for further advancements in game accessibility. Our research analyzes accessibility features in games across genres and platforms (PC, Console, Mobile, VR). Using Interactive Process Modelling (IPM), we map customizable accessibility options available in different games. We present our methodology for conducting interviews with game designers and gamers with disabilities to provide insights into existing options. The research project will result in the development of an accessibility-focused framework for game designers that will enable them to effectively design new customizable accessibility options for their players. Through this research, we aim to contribute to the broader discourse on accessibility and inclusivity in gaming for individuals with disabilities. This work-in-progress paper presents the ongoing progress of our research and invites feedback from the community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".