Disability Evasiveness: Disability Representation in AI-Generated Flash Fiction
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.
Bibliographic record
Abstract
<h3>Abstract</h3> In this research article, Katherine Barron, Ryan B. Collis, Aaron J. Richmond, and Ellouise Van Berkel explore disability representation in flash fiction generated by artificial intelligence (AI). The meteoric adoption of generative AI in K–12 education raises concerns about how tools like OpenAI9s ChatGPT might perpetuate and amplify biases present in their AI training data. In the educational sphere, there is a vital need for both teachers and students to develop critical literacy skills in order to resist discriminatory narratives about historically marginalized groups. The article aims to identify specific expressions of disability-related discrimination in AI-generated short stories. Using critical content analysis and critical disability theory, the authors analyze forty stories about disabled and neurodivergent children generated by ChatGPT-4. The analysis is guided by Connor9s (2017) lists of positive and negative disability representations and Landrum9s (2001) criteria for evaluating story elements. The authors first identify how the stories reflect and reinforce societal biases in the context of disability, ableism, and disableism. They then offer the term <i>disability evasiveness</i> to describe a process where nondisabled people claim to not “notice disability.” The article concludes with suggestions and resources for critical literacy instruction in K–12 settings. This research contributes to disability studies in education scholarship and to ongoing discussions of the use of AI in K–12 classrooms.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it