Disability, Neurological Diversity, and Inclusive Play: An Examination of the Social and Political Aspects of the Relationship between Disability and Games
Bibliographic record
Abstract
This article explores existing connections between disability studies and game studies, and suggests how the two fields might greater inform each other. While existing research explores the use of games to reduce pain and achieve rehabilitative goals, new research on games from a disability studies perspective can also consider the persuasive messages that games advance about disability, and how these messages affect questions of identity, inclusion, and acceptance. By arranging the relationship between disability and games into four topics – therapeutic and educational tools, game simulations, accessible features and controls, and narrative inclusion and identification – this article explores, attempts to address, represent, and simulate autism in digital games. It focuses on Auti-Sim (2013), a simulation exercise, and To the Moon (2011), an adventure role-playing game. Drawing on the writings of autistic activists and existing scholarship on disability simulations, the author considers how these games may influence the player’s understanding of autism at social and political levels, and how these artifacts engage with the overarching goals of disability inclusion and autism acceptance.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".