Bibliographic record
Abstract
Supplemental files to the thesis Kenny Wheeler: Melody, harmony and structure : An analysis of the melodic, harmonic and structural techniques in the compositions of Kenny Wheeler, and the implementation of those techniques into the authors own creative process. Available here: https://ro.ecu.edu.au/theses_hons/1333/ Kenny Wheeler is a Canadian born trumpeter and composer who has gained the respect and admiration of musicians and critics alike for his distinct and beautiful compositions. Despite this acclaim, am extensive search of relevant literature has revealed very little academic study of his compositional technique. Through an analysis of the melodic, harmonic and structural techniques evident in selected compositions by Kenny Wheeler, this dissertation offers insight into what gives Wheeler's music its distinct sound. The influence of these techniques on the author's own creative work is discussed through an exegesis. The dissertation consists of three chapters. The first chapter briefly introduces Kenny Wheeler and discusses why he is an important figure in jazz and the contribution he has made to jazz composition. Chapter two analyses selected Kenny Wheeler compositions, focusing on the areas of melody, harmony and structure. This chapter also explains the methods used in analyzing the melodic, harmonic and structural techniques. The third chapter includes a comparative analysis of the compositional techniques of Kenny Wheeler and the author's own creative work.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.359 | 0.115 |
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".