Where do teachers and learners stand in music education research?
Bibliographic record
Abstract
We offer a multi-voiced performance autoethnography where contemporary music education practices are informed and imbued with the voices of teachers and learners. By dialogically and musically engaging with the very people who live, make music, and engage with learners in music classrooms, we promote contemporary qualitative forms of research and the (re)conception of a sociology of music education as a political and an ethical construction that needs to be grounded in serving communities of music practitioners. Through a pedagogical story, told from the perspectives of music teachers using their own voices, we begin an open conversation about the nature of power structures and struggles in music education research. We invite new possibilities in developing understandings of the complex socio-cultural dynamic of music making, music learning, music teaching, and music researching in all facets of contemporary society. By embracing a broader set of traditions--Arts-Based Educational Research and Creative Analytical Practices--that enable us to go beyond socio-cultural frameworks and orthodox beliefs that currently exist in the music education profession, we seek to (re)form a culturally contextualized, ethos-rooted, sociology of music education.
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 | 0.144 | 0.196 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.058 | 0.150 |
| Scholarly communication | 0.079 | 0.083 |
| Open science | 0.006 | 0.050 |
| Research integrity | 0.021 | 0.028 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".