Bibliographic record
Abstract
Issue 18:2 of the International Journal of Community Music ( IJCM ) includes a literature review of ‘trauma-informed practices’ (Hansen), five research studies (Moufarrej; Fraser; Odena, Mateos-Moreno and Salinas-Maceda; Martin; Pitupumnak and Saibunmi) and book review (Kinnunen) of Dave Camlin’s (2023) recent book, Music Making and Civic Imagination: A Holistic Philosophy . Martin studied a music workshop, ‘Togetherness through music: Uniting Indigenous and non-Indigenous Australia’, aimed at conflict transformation. It is a classic example of a one-off interventionist workshop model. Three of the articles (Fraser; Moufarrej; Odena, Mateos-Moreno and Salinas-Maceda) can be considered as case studies of the ongoing intervention-based work of specific organizations (Common Wheel in Glasgow, the Fayha Choir and Sounds of Change in Syria and EnseñARTE in Cochabamba). Pitupumnak and Saibunmi’s study of the Intergenerational Choir Project at Chiang Mai University also represents an intervention, but of a university–community partnership rather than an NGO or charity-based organization. Community music examples examined by the researchers include choir programming in Syrian refugee camps (Moufarrej), intergenerational choirs in Thailand (Pitupumnak and Saibunmi), a youth empowerment music programme for impoverished youth in Bolivia (Odena, Mateos-Moreno and Salinas-Maceda), a settler–First Nations conflict transformation project in Australia (Martin) and a programme in Scotland for people with mental health issues (Fraser).
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".