The 15th International Conference of Students of Systematic Musicology (SysMus22)
Bibliographic record
Abstract
The International Conferences of Students of Systematic Musicology (SysMus) are a series of interdisciplinary student-run conferences with the aim of promoting intellectual exchange between early-career researchers in various fields of systematic musicology. In 2022, the 15th conference was hosted by the Institute for Psychoacoustics and Electronic Music (IPEM) in Ghent, Belgium, and was held in a hybrid format, allowing researchers to be present and participate in the events in person and online. SysMus22 comprised 43 posters, 23 presentations, 6 workshops, a panel discussion, musical demonstrations, and 3 musical performances. Topics were as diverse as music cognition, psychology, health and well-being, music theory and performance, technology, and philosophy, among others. Adding to the richness and diversity of topics were keynote lectures given by Psyche Loui (MIND Laboratory, Northeastern University), Mendel Kaelen (Wavepaths), and Rebecca Schaefer (Music, Brain, Health & Technology Laboratory, Leiden University). The whole conference was marked by a friendly, warm, and stimulating atmosphere, encouraging the exchange of ideas between all participants and particularly among researchers at the beginning of their academic careers in systematic musicology. This report provides an overview of SysMus22, the topics it covered, and the format it employed, with a critical discussion of the benefits and challenges posed by the hybrid format.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.094 | 0.029 |
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".