The Role of Music in Environmental Education: Lessons from the Cod Fishery Crisis and the Dust Bowl Days.
Bibliographic record
Abstract
Music is a central feature of popular culture and thus can be a powerful force in the classroom. For decades, musicians lamented life in the fishery, first about the toughness of life on the sea, and later the collapse of the cod stocks. Similarly, folk musicians sang of a crisis in culture and environment on the Great Plains of North America during the 1930s. This paper uses lyrics and musical styles to illustrate the role of music in educating young people about ecosystem fragility and the cultural importance of rural resources. This paper begins with a description of the east coast fishery prior to, and following, the announcement of the Northern Cod fishery moratorium in 1992. Following this, the trend towards migration of people from maritime to prairie Canada in search of employment is analyzed through music. Using the 1930s “dust bowl days” as the historic starting point, music is then drawn from the 1930s to the 1990s to describe the ecological and cultural issues facing Great Plains farmers. The paper concludes that music not only provides a rich data source from which to draw, but that it is also a powerful tool for making connections to real life situations in the classroom. Resume
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".