The Essence and Evolution of Song. By Vladimír Úlehla. Translated by Julia Ulehla; edited by Katherine Freeze and Richard K. Wolf.
Bibliographic record
Abstract
Vladimír Úlehla (1888-1947) uses his expertise in the biological sciences to perform an in-depth and ecologically situated study of folk songs from his native Czechoslovakia. His posthumous magnum opus Živá Píseň (Living Song, 1949) chronicled the musical traditions of Strážnice, a small town at the western hem of the Carpathian Mountains at the Moravian-Slovakian border. Informed by four decades of ethnographic inquiry, transcription, and several music-analytical methods, in Chapter VI Úlehla considers the songs from Strážnice as living organisms, links them to their ecological environs, and isolates musical characteristics that he believes correspond to stages of their evolution. He discusses modulation, vocal style, ornamentation, melodic and poetic structure, and identifies a diverse array of musical modes—evidence that he uses to refute the prevailing assumption of the day that folk music was derivative of art music. Citation: Úlehla, Vladimír. The Essence and Evolution of Song. Translated by Julia Ulehla; edited by Katherine Freeze and Richard K. Wolf. Ethnomusicology Translations, no.7. Bloomington, IN: Society for Ethnomusicology, 2018. Originally published in Czech as “Nitro a vỳvoj písně.” In Żivá Píseň. Praha [Prague]: Fr. Borový, 2008[1949].
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.012 |
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".