Jazz Recordings by Ed Bickert and Problems of Stylistic Analysis
Bibliographic record
Abstract
Edward “Ed” Bickert was a fixture in the Toronto jazz scene for almost 50 years, becoming a pervasive influence on both Canadian and international guitarists. This dissertation is an attempt to position Bickert as a unique and masterful improviser. His development as a musician, beginning with his participation in a family band and myriad of playing opportunities in Toronto, mirrors the manner in what I suspect most jazz musicians learn their craft: through performance and continuous home study of challenging music. After a lengthy review of stylistic and musical analysis concepts, the categories in Jan LaRue’s Guidelines for Style Analysis provided an heuristic framework that enabled an exploratory examination of Bickert’s style. The problems of stylistic and musical analysis are many and varied. Primarily, both domains seem suffer from inadequate or ill-defined terminology. Insights into Bickert’s style were organized into LaRue’s categories: Sound, Harmony, Melody, Rhythm, Growth. In the category of Sound, changes in equipment and technique had a significant effect on his style. Bickert’s highly developed harmonic approach involves the use of unusual chord voicings, implying chords with a small number of notes, a flexible approach to chord type, chromatic “planing” and 2/3 note punctuation chords. Paraphrasing and motive/motivic development are important melodic aspects of Bickert’s style, though both usually involve other categories and required the most research due to the inadequacy or lack of consensus of definitions in the existing literature. In the category of rhythm, Bickert’s ability to swing is universally recognized. He is also able to pull against pulse, seamlessly switch between various note values, effectively employ expressive or rubato timing, and shift the metric accents to suggest a different time signature (cross rhythm). LaRue’s concept of lulls: “a condition of relative stability,” seems to have some relevance to Bickert’s employment of punctuation chords in the development of his solos. These basic elements combine to create the effect of structure — or in LaRue’s lexicon, Growth. Three predominant categories of Growth in Bickert’s solos are Paraphrasing; Motivic Development; and Textural Variation.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".