Meaningful Connections in a Comprehensive Approach to the Music Curriculum
Bibliographic record
Abstract
A comprehensive music curriculum is characterized by breadth and depth of musical experience. Curricular breadth involves planning for students' participation in a wide range of musical engagements (singing, playing, composing, improvising, listening, moving, evaluating); exposure to a broad repertoire of works, styles, and genres; and emphasis on the ways that music is organized and constructed through its distinctive elements and forms. Depth of musical understanding comes from pursuing a well-chosen sample of these engagements, music, and elements with regularity and intensity. Through a curriculum that offers both breadth and depth, students become aware of the vast possibilities for lifelong involvement which music affords, and gain the keen satisfaction of knowing some music well. This article begins by addressing key concepts that support a principled foundation for interdisciplinary work in music, and next clarifies distinctions among common terms used to refer to curricular schemes for organizing a connected curriculum. Principles that can be used to guide curricular decisions are provided. The article then explores interdisciplinary work in music from the perspective of (1) the teacher, (2) the learner, (3) the overall curriculum, and (4) approaches and models for generating and organizing interdisciplinary experiences. Whenever possible, it supplements its North American perspective (the US and Canada) with select examples that reflect a more international scope.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".