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Record W22520517 · doi:10.1016/j.ahj.2012.01.003

The possibilities of training the would-be teachers for music teaching through vocal-instrumental teaching

2006· article· en· W22520517 on OpenAlexfundno aff
Emeše Terzić, Данијела Судзиловски

Bibliographic record

VenueZbornik radova Učiteljskog fakulteta, Užice · 2006
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Musicological Studies
Canadian institutionsnot available
FundersNational Institute of Dental and Craniofacial ResearchNational Institute of Allergy and Infectious DiseasesOffice of AIDS ResearchNational Eye InstituteNational Institute on Drug AbuseSouth African Medical Research CouncilNational Heart, Lung, and Blood InstituteNational Institute of Mental HealthFogarty International CenterNIH Office of the DirectorMedtronic FoundationCanadian Institutes of Health Research
KeywordsInstrumental musicSubject (documents)Training (meteorology)Vocal musicMathematics educationTeaching methodPsychologyRhythmMusicalMusic educationPedagogyComputer scienceVisual artsArtAestheticsMusic

Abstract

fetched live from OpenAlex

In this book we examine the current conditions (ways) in achieving successful teaching in vocal instrumental classes using the experience of the previous years. The initial assessment of the musical capabilities of students was an important step in achieving success in this subject. There is also the proof of advancement in vocal-instrumental education during the first semester of this year and also in rhythm parlato play and theoretical knowledge. Many different methods of improvement in education have been examined. .

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0310.004

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 head0.135
GPT teacher head0.270
Teacher spread0.136 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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