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Record W2996348109 · doi:10.5206/eei.v29i3.9388

Young Children with Autism Spectrum Disorder in Early Education and Care

2019· article· en· W2996348109 on OpenAlexaffvenue
Kimberly Maich, Adam Davies, Sharon Penney, Emily A. Butler, Gabrielle Young, David Philpott

Bibliographic record

VenueExceptionality Education International · 2019
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsUniversity of GuelphMemorial University of Newfoundland
Fundersnot available
KeywordsAutism spectrum disorderIntervention (counseling)Early childhood educationPsychologyPsychological interventionAutismConversationEarly childhoodMainstreamingSpecial educationDevelopmental psychologyPedagogyPsychiatry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.011
GPT teacher head0.335
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2019
Admission routes2
Has abstractyes

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