A model for the social neuroscience of music production begins on a dubious note: Commentary on Greenberg et al. (2021).
Bibliographic record
Abstract
Group singing and music-making behaviors that were rapidly adapted to the coronavirus disease (COVID-19) pandemic context suggest to Greenberg et al. (2021) not only a musical solution to pandemic-related social isolation but also the importance of the social neuroscientific side of music. They propose a model of the social neuroscience of music production premised on the view that group singing leads to increased levels of oxytocin (a neuropeptide associated with empathy and social bonding), citing data of Schladt et al. (2017) and Keeler et al. (2015) as support. The present commentary points out that Schladt et al. reported a decrease rather than an increase in oxytocin level following group singing. Further, reference to the work by Keeler et al. (2015) is only partially accurate, and evidence contrary to the oxytocin premise is ignored. Similar inaccuracy is associated with claims for cortisol, another primary component of their model. While the authors are applauded for directing attention to both the social neuroscience of music and the value of group singing, tempering the stated premises associated with the oxytocin and cortisol channels of the model is recommended. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.066 | 0.091 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".