Bibliographic record
Abstract
My research project focuses on folk inspired music of Poland, England, China, and Ireland. In my applied lessons on clarinet, I studied two neoclassical Polish folk pieces, so the question answered in the research is how the two neoclassical Polish pieces compare to folk inspired pieces from other countries. The pieces chosen for this study are mainly pieces that I have heard before. Therefore, I chose the pieces based upon my familiarity with them. Folk music expresses the sounds and rhythms that represent countries all over the world. Over time these sounds and rhythms evolve to reflect the country at that moment. This study will reflect how folk music was implemented into different pieces with a focus on Polish neoclassical folk pieces versus English, Chinese, and Irish folk pieces. There is a detailed analysis focused on two Polish compositions. While the focus of the other global pieces is to allow one to understand how folk music was being used in compositions specific to the country being studied. The purpose of this study is to understand how folk tunes and characteristics can be expressed through larger compositions, and how the different countries and genres approached that. Furthermore, the study compares Polish folk music to the folk music of other countries and where Polish folk composers stand in originality and experimentalism with the composers of England, China, and Ireland.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".