Conference Report : AAWM Special Topics Symposium 2023. Theoretical, Analytical, and Cognitive Approaches to Rhythm and Meter in World Musics
Bibliographic record
Abstract
The study of temporality in world musics raises several questions regarding how music is organized and perceived. The proliferation of theories and points of view to arise in the past two decades attests to the importance of and interest in topics related to this field. The Analytical Approaches to World Musics Special Topics Symposium on Theoretical, Analytical, and Cognitive Approaches to Rhythm and Meter in World Musics brought together forty scholars with fresh approaches to this field. This online conference took place over four days in June 2023. Co-chaired by Lina Tabak and the author of this report, the symposium featured six paper sessions, a special session planned by the organizing committee, a panel discussion organized by the program committee, a book dialogue, and a keynote by Daniel Avorgbedor. Presenters affiliated with six continents took part. The symposium was co-sponsored by the Analytical Approaches to World Musics Journal , the CUNY Graduate Center, the Barry S. Brook Center for Music Research and Documentation (CUNY), and the International Foundation for the Theory and Analysis of World Musics (IFTAWM).
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.201 | 0.078 |
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".