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Record W3091902476 · doi:10.1177/215416472005500304

Enhancing Early Numeracy Skills of Children with Severe Disabilities and Complex Communication Needs

2020· article· en· W3091902476 on OpenAlexaff
John C. Wright, Christopher J. Lemons, Esther R. Lindström, Julia Strauss

Bibliographic record

VenueEducation and training in autism and developmental disabilities · 2020
Typearticle
Languageen
FieldMathematics
TopicCognitive and developmental aspects of mathematical skills
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNumeracyPsychologyCommunication skillsDevelopmental psychologyAugmentative and alternative communicationPedagogyMedical educationLiteracyMedicinePsychiatry

Abstract

fetched live from OpenAlex

There is a lack of research on effective interventions to improve the early numeracy skills of children with severe disabilities—autism spectrum disorder, developmental disability, and intellectual disability—and complex communication needs. While preliminary research suggests the Early Numeracy curriculum is effective for teaching children with severe disabilities, efficacy of this curriculum has not yet been examined for students with co-occurring complex communication needs. Using a multiple probe across participants research design, we evaluated the efficacy of the Early Numeracy curriculum for increasing targeted math skills of four children with severe disabilities and complex communication needs. Results indicated a functional relation between the intervention and improvements in early math skills. Limitations and future research needs are discussed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.033
GPT teacher head0.284
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2020
Admission routes1
Has abstractyes

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