Young Children with Autism Spectrum Disorder in Early Education and Care
Bibliographic record
Abstract
High quality early intervention is a crucial component of supportive and inclusive early childhood education and care (ECEC) and crucial for children with autism spectrum disorder (ASD). For children with ASD, there is limited access to ECEC services and there is little research or writing on the importance of bridging even conversations between the fields of ECEC and special education needs. This paper addresses the importance of starting a conversation by delineating current literature on ASD and early intervention services while making recommendations for how practitioners and policy-makers can consider the needs of young children with ASD in ECEC programming, bringing together clinicians and educators in ECEC settings into broader and closer collaborations. Through investigating current wide-scale reports on ASD in ECEC and inclusive settings, screening, early intervention, and evidence-based interventions, as well as the specific needs of parents of children with ASD, we seek to bring such essential discussions to the forefront. In turn, practitioners can provide supportive early-years environments for children with ASD, as well as early intervention and identification services that support inclusive practices.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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 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".