“Working in Concert”: Examining Music’s Role in Cross-Curricular Education
Bibliographic record
Abstract
The purpose of this presentation is to investigate the power of the cross-curricular teaching and learning practice while exploring the relationship between music and other subject areas in an educational environment. In recent years, the curriculum of countries such as Sweden, Australia, and the United Kingdom, has placed greater emphasis on utilizing the cross-curricular learning and teaching practice. This progressive pedagogy strives to engage students by connecting different curricular areas within an activity or lesson. Students have the opportunity to engage in a deeper form of learning, applying their knowledge and transferring their skills as they discover similarities between distinct subject areas. Additionally, the cross-curricular practice can attract pupils to disciplines that otherwise might not have held their attention. As everyday life is filled with tasks that draw on multiple subject areas, this teaching approach ultimately enables the educational system to better prepare pupils for their future. However, the cross-curricular practice can pose a challenge to teachers who lack confidence in their knowledge of content across the many curricular areas. This is particularly relevant to music education, as many teachers who do not possess a solid knowledge base of skills and techniques shy away from incorporating music into their teaching practice. Nevertheless, cross-curricular learning seems to be a valuable learning process, therefore, I will discuss method of incorporating music into cross-curricular lessons in order to provide
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".