Pedagogical Analysis of the Baroque Period Piano Repertoire: Example of Italy
Bibliographic record
Abstract
The aim of this study is to access editions of Italian Baroque works in place; to examine the availability of these works in terms of gaining techniques for playing in piano education, to gain new works aimed at different pedagogical stages in the field and to acquire new but unknown works in piano education repertoire. This research was carried out with the literature review model. During the first three months of the research, 158 baroque period composers were found among 2173 Italian composers. 50 composers composing on keyboard instruments were reached among 158 baroque composers. For this research study, the library of Dipartimento delle Arti dell’Universita di Bologna the Sala Borsa library, the Giovanni Martini Conservatory library and international museum and library of music in Bologna were visited and the works of composers made on the keyboard instruments were found. The number of works performed by composer on keyboard instruments is quite high. However, according to the objective of the project, it is aimed to perform pedagogical analysis by selecting one work from each composer. For analysis, created to the work evaluation forms prepared by the researcher. 50 works analyzed according to this form. While 48 Italian works can be used in piano education, 6 Italian works are not suitable for piano education. However, in order to use these works in piano education, these must be arranged from organ to pianoforte. An example of this is presented in this study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".