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Record W2260897620 · doi:10.5430/ijhe.v5n1p261

The Physics of Music with Interdisciplinary Approach: A Case of Prospective Music Teachers

2016· article· en· W2260897620 on OpenAlexvenueno aff
Özge Turna, Mualla Bolat

Bibliographic record

VenueInternational Journal of Higher Education · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsnot available
Fundersnot available
KeywordsMusic educationData collectionPsychologyMathematics educationQualitative researchValue (mathematics)MusicologyPedagogyComputer scienceSociologySocial science

Abstract

fetched live from OpenAlex

Physics of music is an area that is covered by interdisciplinary approach. In this study it is aimed to determine prospective music teachers’ level of association with physics concepts which are related to music. The research is a case study which combines qualitative and quantitative methods. Eighty-four students who were studying at the Department of Music Education participated to the study. A data collection instrument which included qualitative and quantitative items with an interdisciplinary approach was used in this research. The collected data were grouped and analyzed by finding percentage value for each item. The findings from the data analysis were interpreted and a case assessment was made. In addition it was asked if it was necessary to take such an education. The findings revealed that most participants were unaware of musical concepts relating to physics and had difficulty in associating these concepts or expressing what they know. However, a great majority thought that such an education was necessary in their department.

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)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0090.004
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.042
GPT teacher head0.292
Teacher spread0.249 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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