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Record W2925718067 · doi:10.48550/arxiv.1904.03656

Ride N' Rhythm, Bike as an Embodied Musical Instrument to Improve Music Perception for Young Children

2019· preprint· en· W2925718067 on OpenAlexaff
Weina Jin, Alissa N. Antle, Diane Gromala

Bibliographic record

VenuearXiv (Cornell University) · 2019
Typepreprint
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsEmbodied cognitionRhythmMusicalPerceptionMovement (music)PsychologyMusic psychologyMusical instrumentMusic perceptionDynamics (music)Cognitive psychologyHuman–computer interactionComputer scienceVisual artsAestheticsArtAcousticsPedagogyArtificial intelligence

Abstract

fetched live from OpenAlex

Music plays a crucial role in young children's development. Current research lacks the design of an interactive system for younger children that could generate dynamic music change in response to the children's body movement. In this paper, we present the design of bike as an embodied musical instrument for young children 2-5 years old to improve their music perception skills. In the Ride N' Rhythm prototype, the rider's body position maps to the music volume; and the speed of the bike maps to the tempo. The design of the prototype incorporates the Embodied Music Cognition theory and Dalcroze Eurhythmics pedagogy, and aims to internalize the 'intuitive' knowing and musical understanding via the combination of music and body movement.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
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 science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.044
GPT teacher head0.200
Teacher spread0.156 · 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 designTheoretical or conceptual
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

Citations0
Published2019
Admission routes1
Has abstractyes

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