Bibliographic record
Abstract
Ukrainian folk music has been embedded into much of the classical music we hear. Mykola Leontovych and Peter Wilhousky are credited for the ever-famous piece Carol of the Bells, an arrangement of a Ukrainian Epiphany carol called Shchedryk (Щедрик). Despite the applicability of Ukrainian folk-inspired music in our society, people are generally unaware of its origin. In fact, researcher Yakov Soroker provides evidence of Ukrainian folk inspiration in various classical pieces being misclassified as Russian, Polish, and/or Hungarian. Ukrainian classical music, for many reasons pertaining to its unstable history, is not well known outside Ukraine and, therefore, is rarely discussed. This has limited potential insights it might bring to those who have interest in its place in Western music. My research explores the influence that Ukrainian folk traditions have had on contemporary classical music. My research has come from gathering and sifting through historical literature about the origins of classical, Ukrainian classical, Ukrainian folk, and other folk music works. I have also listened to selected works and examined the critiques of experts to form conclusions about how composers today have been influenced, knowingly or otherwise, by Ukrainian folk music. Going one step further, in order to provide a deeper, practical insight into the creative process of composers who have been influenced by Ukrainian folk music, I have composed a piece of my own influenced by Ukrainian folklore.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".