The 17th International Conference of Students of Systematic Musicology (SysMus24)
Bibliographic record
Abstract
The 17th International Conference of Students of Systematic Musicology (SysMus24) was hosted by the Centre of Excellence in Music, Mind, Body, and Brain (CoE MMBB) at the Department of Music, Art, and Culture Studies at the University of Jyväskylä, Finland, from June 8 to 10, 2024. Organized in hybrid format, the conference offered a rich variety of presentations and activities featuring 41 talks, 10 posters, 5 workshops, and a panel discussion, as well as an entertaining program of social events. Keynote talks were given by Minna Huotilainen (Professor of Educational Sciences, Director of the Changing Education master's program and Principal Investigator at the Centre of Excellence in Research on Music, Mind, Body, and Brain at the University of Helsinki, Finland), Tuomas Eerola (Professor in Music Cognition and Director of Research at Durham University, UK) and Isabelle Peretz (Professor of Psychology at the University of Montreal, Canada). This conference report offers an overview of SysMus24, reviewing the topics addressed across the conference, and reflecting on the value and timeliness of the hybrid format and its place within the future of academic conferences.
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.127 | 0.040 |
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".