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Record W2995855838 · doi:10.32370/ia_2019_12_26

Specificity in Music-Pedagogical Training Future Music Teacher’s to Conducting Woodwind Ensemble

2019· article· en· W2995855838 on OpenAlexvenueno aff
Wang Zixi

Bibliographic record

VenueIntellectual Archive · 2019
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Teacher Development
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologySet (abstract data type)Task (project management)Literal and figurative languageProcess (computing)Mathematics educationPedagogyComputer scienceManagement

Abstract

fetched live from OpenAlex

The research looked for new ways to improve the management of ensembles and to formulate the readiness of the future leader for the educational process. Students' readiness for professional activity is presented as a complex personal formation. It covers a set of pedagogical influences aimed at different aspects of individual activity. They include mental processes and emotional-volitional reactions that regulate the degree of human activity, motives for its behavior; their awareness creates favorable conditions for the effective formation of the state of "readiness". In the future, the students' organizational skills will depend on the artistic, creative and pedagogical results, which are of particular importance for the conductor of the ensemble. In order to complete the rehearsal task, the conductor must carry out considerable preliminary organizational work. The analysis of its organizational activity makes it possible to distinguish such areas as: pedagogical, artistic, performing, figurative-content and technical.

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)0.004
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 score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.001

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 head0.269
GPT teacher head0.351
Teacher spread0.082 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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