Teaching teachers: Methods and experiences used in educating doctoral students to prepare preservice music educators
Bibliographic record
Abstract
This investigation addressed methods and experiences used to educate doctoral music education students to work as university college professors. Selected faculty representing every institution offering a Ph.D. in music education in the United States and Canada ( N = 46) were sent an online questionnaire concerning (1) the extent respondents believed doctoral music education students should perform student/class observations, teach music education classes, supervise field-teaching experiences, participate in teacher-related activities, and participate in scholarly activities; and (2) whether respondents’ institutions had doctoral music education students perform student/class observations, teach music education classes, supervise field/student teaching experiences, participate in teacher-related activities, and participate in scholarly activities. Respondents strongly believed music education doctoral students should observe and assist in undergraduate classes, supervise field-teaching experiences, and conduct scholarly activities. Respondents placed less value on students interacting with public school teachers, teaching graduate music education courses, and participating in school/college committees. Respondents indicated their institutions did have students perform student/class observations, teach music education classes, supervise field-teaching experiences, participate in teacher-related activities, and participate in scholarly activities. However, interactions with public school teachers, teaching a graduate class, and participating in school/college committees were performed less.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".