MétaCan
Menu
Back to cohort
Record W3030192453 · doi:10.1177/1048371320926603

Integrating Music and Literacy: Applying Invented Music Notation to Support Prosody and Reading Fluency

2020· article· en· W3030192453 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGeneral Music Today · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsQueen's University
Fundersnot available
KeywordsFluencyProsodyMeaning (existential)NotationComputer scienceLinguisticsReading (process)Musical notationRhythmPsychologyExpression (computer science)MusicalMathematics educationSpeech recognitionVisual artsArt

Abstract

fetched live from OpenAlex

This article builds on evidence-based teaching strategies to support a learning experience for third-grade students that integrates language and music. In the language-learning field, “prosody” refers to changes in volume, rhythm, and pitch that add expression and meaning when reading text aloud. When students incorporate prosodic elements into reading, their comprehension of the text is enhanced. In the field of music learning, invented notation allows young music learners to bypass the complexity of traditional notation to authentically express music ideas in a way that is accessible to peers, parents, and teachers. The learning experience described invites learners to use invented notation to represent music nuances within spoken language (prosody). Learners develop their capacity to expressively read aloud while broadening their understanding of composing and the music elements of volume, rhythm, and pitch. An illustrative sample lesson is provided.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.896
Threshold uncertainty score0.999

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.071
GPT teacher head0.260
Teacher spread0.188 · 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