A systematic review of bilingual experiences, labels, and descriptions in autism spectrum disorder research
Bibliographic record
Abstract
There is growing research on autism spectrum disorder (ASD) that examines linguistically diverse samples, increasing research generalizability as many individuals with ASD live in bilingual or multilingual communities. However, bilingualism is not a homogenous experience that can be easily categorized. By clarifying participants' language experiences, research findings can be more meaningful for clinicians and practitioners. In this systematic review, we document how the language experiences of samples with and without ASD were described in 103 peer-reviewed journal articles. We observed that language experiences were characterized using a wide range of labels and descriptions. Approximately half of the studies in this review reported participants' language acquisition history, and 64% of the studies defined language proficiency using standardized measures or parental reports. However, <20% of the studies reported daily language exposure and/or usage of the participants. The diversity in how participants' language experiences were characterized in research reflects the complexity of bilingualism. Yet, to further understand how ASD and bilingualism intersect across studies, to facilitate meta-science development, and to balance generalizability with specificity, reporting common characteristics of bilingual experiences is recommended.
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".