“We’ll All Shout Together in That Morning”: Entrainment and Community in the Toronto Shape Note Singing Group
Bibliographic record
Abstract
This dissertation explores entrainment in Sacred Harp singing using an ethnographic examination of the Toronto Shape Singing Note group. It is accompanied by a qualitative exploration of interpersonal synchrony and affiliation among group members and examines what happens when interpersonal synchrony is destabilized during the COVID-19 pandemic. Entrainment “describes the interaction and consequent synchronization of two or more processes or oscillators.” (Will 2004, 1) Identified first in 1665 by Dutch Physicist Christiaan Huygens, entrainment theory has since been applied widely in mathematics and in the physical, biological and social sciences. Despite obvious applications within the study of music the concept of entrainment has only recently begun to be explored in ethnomusicology. In 2004, Martin Clayton, Rebecca Sager and Udo Will presented an overview of the concept and called for its use in ethnomusicology. They note that there are four modes of data collection that are available to ethnomusicologists when discussing this phenomenon: Ethnographic examination and introspection, musical sound, visible physical behavior (gesture), and physiological processes (heart rate, respiration, brain waves, etc). (Will 2004, 23-24) This project employs the first, second and third modes of data collection and argues that ethnomusicologists can presume the existence of entrainment simply through ethnographic observation. A growing body of research has shown that interpersonal entrainment increases prosocial behavior among those who engage with one another synchronously. (Cirelli, Wan & Trainor, 2014; Trainor, Cirelli, 2015) Using an ethnographic examination of the Toronto Shape Note Singing Group I propose that singing Sacred Harp music increases feelings of affiliation and pro-social behaviour among singers and promotes feelings of affiliation across socio-political bounds.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.020 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".