Influence of Turn-Taking in Musical and Spoken Activities on Empathy and Self-Esteem of Socially Vulnerable Young Teenagers
Bibliographic record
Abstract
This study describes a preliminary test of the hypothesis that, when people engage in musical and linguistic activities designed to enhance the interactive, turn-taking properties of typical conversation, they benefit in ways that enhance empathy and self-esteem, relative to people who experience activities that are similar except that synchronous action is emphasized, with no interactional turn-taking. Twenty-two 12–14 year olds identified as socially vulnerable (e.g., for anxiety) received six enjoyable 1-h sessions of musical improvisation, language games that developed sensitivity to linguistic rhythm and melody, and cross-over activities like rap. The Turn-taking group (n = 11), practiced characteristics of conversation in language games, and these were also introduced into musical activities. This involved much turn-taking and predicting what others would do. A matched control group, the Synchrony group, did similar activities but in synchrony, with less prediction and no turn-taking. Task complexity increased over the six sessions. Psychometric testing before and after the series showed that the Turn-taking group increased in empathy on self-report (Toronto Empathy Questionnaire) and behavioral (‘Reading the Mind in the Eyes’) measures, and in the General subtest of the Culture-Free Self-Esteem Inventory. While more work is needed to confirm the conclusions for relevant demographic groups, the current results point to the social value of musical and linguistic activities that mimic entrained, tightly coordinated parameters of everyday conversational interaction, in which, at any one time, individuals act as equal participants who have different roles.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".