Bibliographic record
Abstract
Music composition is an art of courage and thoroughness. After nearly four years of playing jazz with Queen’s music professor Greg Runions (winner of the 2006-07 Queens Music Department Teaching Award) and studying music theory and analysis, I have recently delved into the fascinating art of writing original music in the broadly defined jazz idiom. The opportunity to give something back to the creative music community has been both humbling and inspiring. Through Prof. Runions impressive experience as a prolific local composer, I have learned about the challenges of connecting melodic ideas with music harmony that both pleases and challenges the listener. In two semesters of study, we have explored jazz arranging for a variety of instrument groups, the complex art of chord extensions and modulation, and writing melodies over chord progressions that are memorable and enjoyable. I have produced a dozen songs in lead sheet format, similar to the way music is found in jazz performance fake books. Some of these pieces have been fleshed out to cover a wide range of instrumental performance, including solo piano, jazz combo, vocal jazz ensemble, and full jazz ensemble. I plan to continue this process throughout my life as new inspiration and musical situations arise, seeking to grasp the expressivity and enjoyment that music instills in us all.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.169 | 0.095 |
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".