Playing Autistic: A Critical Examination of Autistic Characters On-Screen from Hollywood to Hallyuwood
Bibliographic record
Abstract
Able-bodied and neurotypical actors are accustomed to receiving nominations for or winning a People's Choice Award, a Primetime Emmy Award, a Golden Globe Award, or an Academy Award due to their on-screen performances as autistic, neurodivergent, or disabled characters (Johnson, 2012).This research explores the critique of able-bodied and neurotypical actors who play autistic female and male coded youth and adult characters in a Hollywood film, medical genre, and a K-Drama.I examine key scenes from Sia's Music (2021) in comparison to key scenes from eight episodes of ABC's The Good Doctor (2017-2024) and ASTORY's Extraordinary Attorney Woo (2022).I employ the interdisciplinary approaches of Critical Autism Studies, Discourse Analysis, Disability Media Studies, reception theory, screen studies, and qualitative content analysis to better understand the construction and reception of autistic characters across media forms and audiences in various cultural contexts.Ableist and neurotypical discourses were identified encompassing themes such as feigning autism, neurotypical splaining, and K-Drama autism.Counter-discourses which celebrated anti-ableism and identity-first terminology also surfaced; such discourses included themes of autistic acceptance and pride, as well as the defiance of autistic archetypes.This research contributes to the emerging interdisciplinary field of Critical Autism Studies, exploring a gendered and cross medium comparison of autistic characters between Hollywood and Hallyuwood media texts.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.018 | 0.018 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".