An Analysis and Interpretation of Barbara York's Concerto For Tuba And Orchestra :“Wars and Rumors of War”
Bibliographic record
Abstract
[[abstract]]芭芭拉·約克〈Barbara York 1949-2020）出生於馬尼托巴省溫尼伯，逝世於堪薩斯州匹茲堡。為加拿大裔美國作曲家兼作詞家，於2004年受低音號演奏家邁克爾·費舍爾（Michael Fischer）和博伊西(樹城)州立大學交響樂團委託創作這首《低音號協奏曲戰爭與謠言》。 全文共分為六個章。第一章「緒論」，闡述本文的研究動機與目的及研究範圍與方法。第二章「芭芭拉·約克」，研究作曲家生平、作品、風格。第三章「低音號的歷史與協奏曲」，介紹低音號的演變發展、協奏曲式與知名協奏曲。第四章「樂曲分析」，各樂章之曲式結構分析。第五章 「演奏詮釋」，介紹筆者如何詮釋此協奏曲。第六章「結論」，對本論文作探討總結。 Barbara York, born in Winnipeg, Manitoba, 1949-2020, passed away in Pittsburg, Kansas. She was a Canadian-American composer and lyricist. In 2004, she was commissioned by tubist Michael Fischer and the Boise State University Symphony Orchestra to compose the Concerto for Tuba and Orchestra : "Wars and Rumors of War". The full text is divided into six chapters. The first chapter, Introduction, expounds the research motivation and purpose of this paper and the scope and method of the study. The second Chapter " Barbara York", a study of the composer's life, works, and style. The third Chapter , " The History of the Tuba and Concerto ", introduces the evolution and development of the Tuba, concerto form, and famous concertos. The fourth Chapter, "Music Analysis", the musical structure of each music analysis. The fifth Chapter, "Playing Interpretation", introduces the author's personal interpretation of this concerto. The sixth Chapter" "Conclusions" is discussed and summarized in this paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.016 | 0.022 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".