Bibliographic record
Abstract
Music, like language, is a uniquely human experience, ubiquitous across human cultures and across the human life span.Musical capacity appears early in evolution and it seems to be innate to most of the human population. Neurobiological studies of music perception and music performance profoundly affect the brain, in an acute and chronic way, by modulating networks involved in cognition, sensation, emotion, reward, and movement corresponding to the empirical findings why people listen to music: pleasure, self-awareness, social relatedness, and arousal and mood regulation.Most intriguing is “salutogenic” effect of musical activities, such as instrumental and choral “musicking” (particularly in non-professional musicians), both on the individual level and in populations. Musical training can promote the development of non-musical skills as diverse as language development, attention, visuospatial perception, and executive functions.Music is also a prophylactic resource, it improves the bonding of mother and child. There is a wide range of therapeutic domains and disorders where musical interventions improve the outcome. As an example, familiar music has an exceptional ability to elicit memories, movements, motivation and positive emotions from adults affected by dementia.Considering that one of the most important problems in biomedicine is “understanding what is to be human” then “music should be an essential part of this pursuit” – of an understanding of the whole person. Despite evidence of significant effects of music on health and well-being - music is not well present in current re-humanization of medicine
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".