Bibliographic record
Abstract
This article examines the early practicum experiences of a teaching candidate struggling with classroom management in a tough initial student teaching placement and his relationship with an advisor who coached him through the difficulty of getting his elementary school pupils to attend to his lessons. The coach advised the student teacher to use his former success in the music industry as a way of finding his legs as a teacher, so the two worked together with music and singing with the children as a means of establishing classroom control and management. The nuances of learning in the practice setting through demonstrating and telling, listening and imitating are examined. The coach and student teacher ended up becoming friends and playing together in a part-time band. Four years after their meeting and work together in the difficult practicum, the two had an opportunity to revisit their musical work with children and the article includes a description and examples of the rich applications of music in the current practice of the teacher, now working at the same school where the practicum occurred. The use of music and singing with children had advanced from being an instrument of control to being a tool for further their learning of various topics in science and of establishing community and camaraderie in the school. The article is couched in an argument that suggests the rhetoric of best practices and measureable outcomes for all students may dilute teaching candidate and teacher educator’s conceptions of being and becoming a teacher, and recommends that we attend to ways in which our discourses about teaching and learning might assist young teachers in thinking about the role of their own interests and talents in their careers as teachers.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.036 | 0.015 |
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".