Formation of Arithmetic Musical Competence in Students
Bibliographic record
Abstract
Objective: The purpose of this study is to form the arithmetic musical competency of students on the basis of Al-Farabi’s theoretical musical heritage. In this context, the work “The Great Book of Music” is of great importance. In this work, he gives not only a scientific explanation of the origin of sounds as properties of matter but also gives an idea of the arithmetic principles of the emergence of harmony and musical melodies. Background: Musical competence is described as an ability and an aptitude to adequately perceive and emotionally respond (react) to music, to transfer musical perception into the ability to think using artistic images. In school, its formation occurs during the study of a discipline "Music", among special subject competencies that form within this subject an arithmetic musical competence can be distinguished. Method: Judging from the musical theory composition method, Al-Farabi suggests an innovative method to improve the musical development of students with intellectual disabilities which consists of the individual preparation of a training plan. Results: As a result of this study, it was determined that the problem of the formation of arithmetic musical competence in the learning process is relatively new and insufficiently studied. Arithmetic musical competence can be formed on the basis of teaching the method of the musical theory of composition Al-Farabi. Conclusion: The experiment results allows for the conclusion about the advantages of the formation of musical competence of students via knowledge of music theory.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".