Bibliographic record
Abstract
Engaging in Community Music: An Introduction focuses on the processes involved in designing, initiating, executing and evaluating community music practices. Designed for both undergraduate and graduate students, in community music programmes and related fields of study alike, this co-authored textbook provides explanations, case examples and ‘how-to’ activities supported by a rich research base. The authors have also interviewed key practitioners in this distinctive field, encouraging interviewees to reflect on aspects of their work in order to illuminate best practices within their specialisations and thereby establishing a comprehensive narrative of case study illustrations. Features: a thorough exploration and description of the emerging field of community music; succinctly and accessibly written, in a way in which students can relate; interviews with 26 practitioners in the US, UK, Australia, Europe, Canada, Scandinavia and South Africa, where non-formal education settings with a music leader, or facilitator, have experienced success; case studies from many cultural groups of all ages and abilities; research on life-long learning, music in prisons, music and ritual, community music therapy, popular musics, leisure and recreation, business and marketing strategies, online communities – all components of community music.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.012 |
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".