Tunes from the Land: Can Folk Music Cultivate Awareness and Respect for Nature?
Bibliographic record
Abstract
This basic qualitative research study explores ecoliterate approaches to instrumental music education, focusing on cultivating environmental awareness through using folk music to address a disconnect from nature in educational contexts in the United Kingdom. Drawing from both research literature and personal experience, the study involved planning and delivering violin lessons designed to foster ecoliteracy in young people. By focusing on the intersection of environmental and folk music education, the project aimed to develop a closer relationship between students and their environment: in particular, a sense of place. Two violin students, aged 12 and 14, were selected as participants for the study. Data was collected through informal post-lesson conversations, which were audio-recorded and transcribed, alongside lesson observations and reflections, then analysed using thematic analysis. The key themes that emerged were: Oral Tradition, Connection to Place, Instruments, Teaching Environment, and Student Development. Examining the students' experiences provided insights into how instrumental music teachers can incorporate ecoliteracy into their pedagogy. This study offers practical suggestions to foster ecoliteracy, such as the use of oral learning, improvisation based on natural sounds, and exploration of local music. These findings will be of interest to music educators seeking to enrich their curriculum with the aim of supporting a more ecologically aware generation.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".