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).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".