What Happens When People Sing? A Community of Voices: An Investigation of the Effects of Belonging to a Choir
Bibliographic record
Abstract
This is the story of people who come together regularly to sing; a community of singers who partake in a meaningful artmaking experience. Why do people spend time and money on arts activities when current economic and educational values suggest that other pursuits are more worthy? What can be learned about engagement in arts experiences from this group? Literature and research inform us that the arts are a means of knowing and learning; that they contribute to a culture of permanence; and that they are a means of connecting humans to the world beyond. This study is designed to get at the heart of the experience of the individual singer who belongs to a specific choir. Case study, grounded theory, and narrative inquiry methods were employed, using the ethnographic tools of the video and audio recorded field events, the recorded and transcribed interview, recorded and transcribed focus group interviews, field notes, and questionnaires. The individual voices of the participants were captured, studied, and analyzed, and conclusions in the form of hypotheses were introduced. The data presented themselves in a matrix of layers and clusters. The study revealed that participants viewed their participation in the choir as Community, Self-Identity, Means of Restoration and Healing, and Means of Developing Discernment and
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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".