An ethnography of secondary school student composition in music : a study of personal involvement within the compositional process
Bibliographic record
Abstract
This study deals primarily with the way secondaryschool students use music to express their personal, inner feelings through composition. Because the topic of personal expression through music composition is an elusive one, my methodology borrows and combines analytical tools from the fields of phenomenology, hermeneutics, semiotics, ethnography and English language arts, and incorporates these tools in the exploration of student's processes, of self-expression through music composition. For a period of several weeks I worked with a sixteen-student sample group,in a Vancouver high school. Each of the students handed in a musical composition. Four of the students granted me interviews in which they shared their views of their compositional processes and the resulting musical works. By the conclusion of the study, the students had displayed the ability to communicate certain ideas, images and emotions, and express themselves by articulating their own unique sense of being through their musical compositions. Furthermore, the students demonstrated a level of musical awareness which has very little to do with the type of proficiency-based music learning that is prevalent in many of today's music education classrooms. Most importantly, however, this study spawns a methodology which examines students' compositional processes rather than their finished musical products.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".