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Record W2612422205

Music Education with Autistic Students in the Mainstream Elementary Classroom

2017· other· en· W2612422205 on OpenAlexaffabout
Jacqueline Meyer

Bibliographic record

VenueTSpace (University of Toronto) · 2017
Typeother
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMainstreamPsychologyPedagogyMathematics educationPolitical science
DOInot available

Abstract

fetched live from OpenAlex

The present qualitative research study examines the question: How do primary/junior teachers use music education with Autism Spectrum Disorder (ASD) students in the mainstream classroom? This study helps to understand teachers’ use of music education as an educational strategy for students with ASD and if it is being used to its full potential in the classroom. Data collection was done through semi-structured interviews with three Ontario certified primary/junior teachers working in the Peel District. Data revealed four main themes: Teachers’ comfort level and experience in music education with ASD students, Teachers’ beliefs of the benefits in incorporating music education in their classroom, Music education resources, strategies, and materials for students with ASD, and Challenges teachers have experienced with music education and ASD students. Implications and recommendations for the education community and teacher practice are discuss and stress the importance of challenges teachers face in connecting with the supports and resources to meet their students’ needs.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.162

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0080.004
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.028
GPT teacher head0.251
Teacher spread0.223 · 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 designQualitative
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

Citations3
Published2017
Admission routes2
Has abstractyes

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