Expressive Virtuosity on Accordion: The Performance Techniques of Joseph Macerollo, O.C.
Bibliographic record
Abstract
Since the 1970s, Joseph Macerollo’s accordion teaching has attracted hundreds of students from at least 18 countries, and he has given hundreds of world premieres with an emphasis on contemporary chamber music. A pioneer of using accordion in a contemporary music landscape, Macerollo developed a multitude of precise body techniques for integrating the accordion’s sound into diverse musical environments. His techniques incorporate all parts of the upper body (and occasionally the legs) to achieve superior versatility and awareness in the performer’s production of tone, phrasing, articulation, projection, and the ability to alter how ‘time’ is perceived by the listener—all of which I call collectively Expressive Virtuosity. Macerollo summarizes his method in a four-point model—Time, Tone, Flow, and Space. Examples of his unconventional techniques include playing the left hand ahead of the right, using multi- dimensional bellowing motions, creating rhythmic points using combinations of finger, bellows, and body techniques, and moving around the instrument. These techniques have the potential to revolutionize how accordion is taught at the university level, and the central goal of this thesis is to document and explain Macerollo’s unconventional techniques. To offer additional perspectives on Expressive Virtuosity, I also conducted interviews with renowned Professors Geir Draugsvoll and Claudio Jacomucci, including in-depth discussions of performing as an orchestral soloist and using the Alexander Technique on accordion. The recent rise in articles related to accordion performance and pedagogy shows that the academic accordion community is searching for new techniques that will inspire greater creativity in relationships with composers, within pedagogy, and on the concert stage. After a lifetime of performance experience and teaching, Macerollo’s techniques will help raise concert accordion playing to the next level and acknowledge Canada’s most important contribution to date in this field.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".