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Record W4410781742 · doi:10.1080/10447318.2025.2508307

Content Analysis of Basic Accessibility Features in 30 Top Played Video Games

2025· article· en· W4410781742 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Human-Computer Interaction · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Accessibility for Disabilities
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceVideo gameMultimediaHuman–computer interaction

Abstract

fetched live from OpenAlex

Game features can create access barriers that undermine player experience for all users, and impede some users from playing. While segments of the games industry have made headway in reducing the number of barriers that players face, the extent to which these barriers remain present in contemporary commercial game titles has been largely unmeasured. We address this problem by measuring the implementation of accessibility features, and where games can improve. We conducted a content analysis of 30 top-played Steam games using the “basic accessibility features” from the Game Accessibility Guidelines. Our results show a positive trend: nearly two-thirds of basic accessibility features were fully implemented. However, there remains substantial room for improvement, as basic features are intended as a baseline, not a gold standard. Our work highlights that under-implemented accessibility features could be better supported at low cost and effort—highlighting a clear path to improvement for games industry stakeholders.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.047
GPT teacher head0.401
Teacher spread0.354 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it