Transition Into the Teaching Profession: Induction and Mentoring Issues Surrounding Secondary Music Teachers
Bibliographic record
Abstract
The purpose of this study was to investigate the issues surrounding the transition into the teaching profession by specifically focusing on teacher induction and mentoring issues while explicitly addressing matters of concern by secondary music teachers in a large suburban school board in southern Ontario. Participants included beginning teachers with fewer than 5 years of teaching, mid career teachers with between 6 and 15 years of instruction, and experienced teachers with more than 16 years of practice. The processes of mentoring and inducting new teachers within the board were examined, along with their relationships between protégés, mentors, and administrators. Further, internal and external programs specifically designed and implemented for newer music teachers were scrutinized and discussed. Data were collected through 16 personal interviews as well as an analysis of key documents and literature on the subject. The findings suggest that although the necessity of mentoring and induction processes has begun to be recognized, there exists a fundamental relationship between mentoring and induction and the affect of the professional attachments to mentoring; the institutional and administrative supports that are enabled; and essential processes and practices between mentors and protégés. Together these three arms combine to support successful induction and mentoring initiatives that will help ease the transition into teaching.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".