One world, one autism? A commentary on using an intersectionality framework to study autism in low-resourced communities
Bibliographic record
Abstract
In the short history of autism research, one of the most consistent findings across studies is that there are no differences in prevalence rates or clinical expression of autism as a function of culture, ethnicity, or geographical location (Nowell et al., 2015).On the contrary, there is increasing evidence pointing to social determinants of health (i.e., poverty, access to health care, educational level social networks, and neighborhood support) having an impact on autism symptom expression and overall quality of life of autistic individuals1 (Magana et al., 2013).Over the last years, there has been an increase in autism spectrum disorder (ASD) research, but the majority of studies conducted have been in high-income countries, mainly the USA, UK, and Canada (Elsabbagh et al., 2012; Sweileh et al., 2016;Zeidan et al., 2022).Furthermore, those studies conducted in low-and middle-income countries (LMIC) do not have a good representation of autistic individuals living in underserved communities.For example, one 1 We are aware of the diverse opinions regarding the terminology used to refer to individuals on the spectrum.We have elected to use identity-first instead of person-first language following suggestions to avoid ableist language (Bottema-Beutel et al., 2021).
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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 | 0.052 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.044 |
| Scholarly communication | 0.011 | 0.033 |
| Open science | 0.013 | 0.012 |
| Research integrity | 0.046 | 0.099 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".