First Do No Harm: Suggestions Regarding Respectful Autism Language
Bibliographic record
Abstract
Nationally and internationally, efforts are ongoing to promote diversity, equity, and inclusion in healthcare and other fields. These efforts require consideration of ways in which language and assumptions impact individuals and communities. The autism and disability spheres are no exception. Indeed, the mental health of autistic people is predicted by the degree to which they feel society accepts them as autistic.1 Thus, we believe discourse that disparages autism could be harmful to autistic people’s well-being. Autistic individuals who face further stigma and discrimination due to other intersectional identities might be particularly vulnerable. Unfortunately, autism research and practice have traditionally used disparaging language grounded in the medical model.Some might object that alternatives to traditional medical model terms are subjective or unscientific. However, we believe traditional terminology is heavily laden with subjective value judgements. For example, the traditional term “disorder” has a decidedly negative connotation. It also implies that individuals’ own characteristics are responsible for their challenges, and it suggests a need to eliminate this disorder. In contrast, the more nuanced word “disability” allows both individual characteristics and societal or contextual barriers to contribute to challenges. The term disability thus appears to be both more scientifically appropriate and less stigmatizing toward a vulnerable population than disorder.In Table 1, we list various traditional terms and concepts that we believe are problematic, along with suggested replacements. We also suggest that practitioners and researchers balance a focus on autistic individuals' challenges with discussion of their strengths and potential. This balanced approach may be especially important for families of young children whose futures may be unclear and a source of considerable anxiety to caregivers.Furthermore, researchers and practitioners should be aware of an ongoing debate between supporters of identity-first (“autistic person”) and person-first (“person with autism”) language. Many autistic individuals support identity-first language2,3 and some fear that person-first language reflects negative attitudes toward autism.4 However, others endorse person-first language.2,3 The term “person on the autism spectrum” is often the most preferred term among autistic individuals and other stakeholder groups,2,3 and this verbiage is typically found to be acceptable by proponents of both person-first and identity-first language. Practitioners should ask about and respect the language preferences of individuals “on the spectrum” who can articulate their views.Overall, in light of concerns that typically-developing people struggle to understand autistic perspectives,5 we urge practitioners and researchers to strive to have empathy for how their language sounds to autistic people. We also suggest it can often be helpful to ask oneself if one would use similar phrasing with other marginalized communities. We feel that there needs to be a shift toward “cultural humility” and willingness to learn from autistic people about autistic identities and how to promote autistic well-being.Practitioners and researchers interested in a more detailed discussion of appropriate autism terminology should refer to Bottema-Beutel and colleagues.6 We provide definitions of neurodiversity terminology (eg, neurodiverse, neurodivergent) in Supplemental Table 2.
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How this classification was reachedexpand
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; both teacher heads agree on what is shown here.
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".