Bibliographic record
Abstract
Over the course of three and a half days in June of last year, several hundred psychologists, musicians, neuroscientists, music therapists, physiologists, educators, physicians, students, and many others representing more than 20 countries came together on the McGill University campus in Montreal for the third Neuroscience and Music ("Neuromusic" for short) conference.Continuing with the now clearly successful formula, this varied group of persons exemplifies the enduring and expanding interest in pursuing research on music, its neural bases, and its applications from a scientific viewpoint.Previous meetings on the neuroscience of music have been promoted and organized by the Mariani Foundation in Venice in 2002, and in Leipzig in 2005, with the themes "Mutual Interactions and Implications on Developmental Functions," and "From Perception to Performance," respectively.These meetings are all an outgrowth of an initial scientific conference sponsored by the New York Academy of Sciences in 2000, entitled "The Biological Foundations of Music."From this initial endeavor comes the collaboration that has resulted in the publication of the proceedings of these meetings as Annals of the New York Academy of Sciences.The theme of Neuromusic III was "Disorders and Plasticity," a fitting continuation of the previous meetings, which focused to a greater degree on basic-science developments.The choice of theme reflected the organizing committee's desire to highlight recent developments in the clinical domain, as well as to encourage more and better research in this area in future.Furthermore, the idea that music can serve as a model system to study plasticity is now very widespread, and the theme of this meeting also reflected this consensus.One innovation in the organization of this year's meeting is that all of the symposia were generated by those proposing them, rather than being the product of "top-down modulation" from the scientific committee.This approach allowed us to provide an opportunity to present new ideas or research to anyone who wanted to, thus ensuring that the meeting would not become the domain of a select few.The committee had a difficult job in the selection of symposia because the submissions were all of high quality and in keeping with the desired theme; after some adjustments, the final program included all of the main topics of interest that were submitted, and we are pleased to present a substantial selection in the present volume.Several observations are in order concerning the meeting, which are also reflected in the papers presented here.First, there seemed to be general agreement that the era of music neuroscience research has now arrived: it is no longer necessary to convince those outside the area of the importance or interest of this research.On the contrary, persons from other domains, as well as the general public, increasingly show enthusiastic interest and in many cases want to join in the enterprise.This feeling is confirmed by the overall quality of the research presented at the meeting, which continues to show improvements in quality and in scope over the last decade.Indeed, many participants and observers at the meeting spontaneously commented that the posters, often presented by some of the younger investigators and by students, were of particularly high interest and originality,
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.588 | 0.420 |
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".