Representation Matters: Race, Gender, Class, and Intersectional Representations of Autistic and Disabled Characters on Television
Bibliographic record
Abstract
Media reflect and affect social understandings, beliefs, and values on many topics, including the lives of autistic and disabled people. Media analysis has garnered attention in the field of disability studies, which some scholars and activists consider a promising approach to discussing the experiences of – and for promoting social justice for – autistic people, who remain underrepresented on scripted television. Additionally, existing portrayals often rely on stereotyped representations of disabled individuals as objects of pity, objects of inspiration, or villains. Television may also serve as a primary source of public knowledge about disabled people and the concept of disability. It is therefore essential that such portrayals avoid stigma and stereotyping. We take a disability studies lens to critically analyze and compare representations of diverse people, who may sometimes be conflated in the popular imaginary, across television series about autistic characters (Atypical, The Good Doctor), those with cerebral palsy (Speechless, Special), and a character with fetal alcohol spectrum disorder (Shameless). We employ an intersectional analytic framework to problematize representations of autistic and disabled people, using television, feminist, and critical disability studies literatures. We analyze how the formal structure of television storytelling can either enable or disable its characters, as well as how portrayals of disability that display a sensitivity to concerns raised by critical disability discourse do not necessarily display the same sensitivity when they intersect with marginalized experiences of gender, sexuality, race, and class.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".