”Not a trouble”: A mixed-method study of autism-related language preferences by French-Canadian adults from the autism community
Bibliographic record
Abstract
Finding a consensual term for persons diagnosed with autism spectrum disorder has recently been debated in the scientific literature. Considering the stigma associated with using terms deemed offensive, it is paramount to address autism respectfully and consensually. As of now, this study is the first to consult French-Canadian participants beyond autistic people themselves. This mixed-method study aimed to document and understand the preference and offensiveness of terms used to refer to persons living with autism. Participants ( N = 327) were adults who self-identified as part of the autism community (i.e., autistic person, family or friends, professionals, or clinicians). By means of an online survey, they rated and ranked six terms used to designate an autistic person. Participants also explained their ranking. Results show no consensus for the use of any one specific term. A clear dichotomy appears between autistic adults’ and professionals’ preferences in terminology. The latter prioritized terms related to the medical model, whereas autistic persons preferred using identity-related language. Among all respondents, Autistic person was the most preferred and least offensive term. Thus, we suggest asking for the concerned person's preference whenever possible or using terminology preferred by the majority when this cannot be done. Lay abstract Recent studies and editorials by autism researchers suggest distancing ourselves from the medical terms used to name autism. Considering the stigma associated with using terms deemed offensive and the influence culture has on language, it is paramount to address autism respectfully and consensually in a culturally sensitive way. As of now, data on the French-Canadian population has yet to be collected. Participants ( N = 327) who self-identified as part of the autism community (i.e., autistic person, family or friends, professionals, or clinicians) completed an online survey. They rated and ranked six terms used to designate an autistic person. Participants also explained their ranking. Among all respondents, Autistic person was the most preferred and least offensive term by most respondents. An apparent dichotomy appears between autistic adults’ and professionals’ preferences in terminology. The latter prioritized terms related to the medical model, whereas autistic persons preferred using identity-related language. However, considering the French syntax, the latter justified their preference based on identity-first and person-first principles. Because results show no consensus for one term, we suggest asking for the concerned person's preference whenever possible or using terminology preferred by the majority when this can’t be done.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".