High school music programmes as potential sites for communities of practice – a Canadian study
Bibliographic record
Abstract
My exploration of the nature of the high school music experience was undertaken with 33 young adults who had graduated from high school one to six years previous to the data collection. All of these participants had been involved in their school music programmes and 30 had not continued formal music study following graduation. One might predict that prevalent themes from their reflections about high school music would include the enhancing of self-worth, the experiencing of a sense of belonging, and the recalling of feelings of pride and pleasure derived from music making. While these themes were indeed articulated, what emerged most strongly from the data analysis is the enormous importance of community as the umbrella for self-making and music making. Importantly, the community these participants were recalling was not uniformly experienced. To better understand this variation, I examine the theoretical concept of ‘community of practice’ (Lave and Wenger 1991; Wenger 1998). I also consider what the participants meant by ‘fun’ and ‘enjoyment’, two terms that peppered their conversations about their high school music experiences. The community of practice theory together with Csikszentmihalyi's (1990) definition of happiness emboldens me to suggest conditions necessary for a community of practice to develop among students in a high school music programme.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.036 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".