Bibliographic record
Abstract
The past two decades have witnessed substantial growth in a ention toward the intersection of performance and analysis, notably through activities sponsored by the Performance and Analysis Interest Group, special sessions at SMT meetings, and thought-provoking articles and monographs.(1) This increased a ention notwithstanding, few conferences devoted specifically to performance and analysis have occurred in North America.(2) Furthermore, and perhaps more urgently, the voices of performers themselves are still not thoroughly integrated into our discipline's research in performance and analysis.These observations underpinned the motivation behind Dialogues: Analysis and Performance, a three-day symposium held October 7-9, 2021, at the University of Toronto.[2] This symposium, dedicated to contemporary music and musical practice, aimed to encourage greater sensitivity to what the theory and performance communities can offer one another in the domains of research and practice.The schedule featured workshops, papers, lecture-recitals, concerts, and keynote presentations.Keynote lecture recitals were given by percussionist Steven Schick (UC San Diego), flautist Claire Chase (Harvard), and accordionist Andreas Borregaard (Norwegian Academy of Music).Ryan McClelland and Russell Hartenberger (both of University of Toronto) delivered a lecture on Steve Reich's Sextet (1984) followed by an energetic performance of the work by the U of T Percussion Ensemble.Robert Hasegawa (McGill) convened a masterclass in analysis and performance of electroacoustic music, and Daphne Leong (CU Boulder) hosted a workshop on collaborative research between theorists and performers.This variety of offerings created an equitable balance between wri en and performed research.[3] The call for proposals sought submissions as lecture recitals and papers and was built around six related questions:1. How can the concerns, choices, and pursuits of music performance inform the practice of music analysis?2. How can the concerns, choices, and pursuits of music analysis inform the practice of music performance?3. What sites of intersection provide promise for collaborative research between music theorists, musicologists, and performers?conference presentations (though holders of major research grants from this organizations can use some of their funding to cover travel costs).
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.655 | 0.440 |
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".