Bibliographic record
Abstract
Since its birth in the beginning of the twentieth century, jazz music has had an immense impact on the world. As jazz began to grow, many sub-genres developed within jazz, incorporating elements of blues, funk, rock, free music, and complex harmony. As a jazz musician myself, something always fascinates me is the diversity within the genre. With the development of steaming platforms such as Spotify, it has never been easier to discover the immense amount of jazz that musicians have gifted to this world. The popularity of jazz music has been in steady decline since the 1960s. This has resulted in many of the lesser-known jazz musicians to go unrecognized by the public as a “jazz greats,” unlike names everyone knows, such as Miles Davis, Duke Ellington, and John Coltrane. The Canadian born trumpet and flugelhorn player Kenny Wheeler has always stood out to me as an incredibly versatile and original composer, who has stretched the boundaries of jazz music. Wheeler has had an enormous impact on my musical studies–pushing me to compose more than I have ever before, and making me question the limits of jazz composition, like he did in his career. Studying Wheeler’s life and composing is a vehicle for me to work on my own composing and musicianship. Thorough analysis of Wheeler’s music has led me to many ideas that I transfer into my own music, and has given me context into what Wheeler was possibly thinking when he sat down to write his inventive music. As the last portion of this project, I have prepared a performance of five original Kenny Wheeler songs for jazz ensembles of varying size, from trio to septet. I personally transcribed and arranged all of the music for the concert and presentation on Kenny Wheeler. Doing this allowed me to get inside the mind of the great composer, and get a deeper look at the possible inspiration for his writing style. For one of my arrangements, I decided to broaden my own arranging skills by creating a sound that was unlike what Wheeler composed, while still using his melodic and harmonic ideas as scaffolding. This was no simple task, because Wheeler’s music is already so individual to him and him only; it is a great challenge to arrange his music to sound different than the iconic recordings he released decades ago.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".