Things ain't what they used to be: a look at the emergence of female flutists into the jazz world
Bibliographic record
Abstract
My thesis is designed to explore the challenges female jazz flutists have faced breaking into the jazz world. This is important because not many flutists, especially females, are involved in the jazz world as soloists, bandleaders, or composers. The primary purpose of this thesis is to increase understanding and awareness of the flute as a jazz instrument. I also hope to increase interest in jazz in my flute colleagues. I researched the history of the flute in jazz, focusing on female jazz flutists; specifically, Ali Ryerson, Holly Hofmann, and Jamie Baum, among others. I looked at their influences and background in jazz, what struggles they may have overcome, and what accomplishments and contributions they have made in their field. I also conducted personal interviews with female jazz flutists in person at the 2014 National Flute Convention, by telephone, and by email to increase my understanding and to hear firsthand what it is like to be a female flutist playing jazz. In addition, as part of my goal to increase knowledge of and interest in the flute as a jazz instrument, I chose a standard jazz band chart, “Things Ain’t What They Used to Be”, and arranged it for jazz flute big band. I worked with the Northern Iowa Flute Choir and volunteer members of a standard rhythm section to rehearse and perform the work at my thesis presentation.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.014 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".