Bibliographic record
Abstract
This study was designed to estimate the magnitude of retention, migration, and attrition of music teachers; the transfer destinations of those who migrated; the career path status of those who left; and the likelihood that former music teachers would return to teaching. Data, which were analyzed for music ( n = 881) and non-music teachers ( n = 17,376), came from the 1988—1989, 1991—1992, 1993—1994, and 2000—2001 administrations of the National Center for Education Statistics's Teacher Follow-up Survey, a national survey designed to compile comprehensive data concerning changes in the teacher labor force. Results indicated that between 1988 and 2001, 84% of music teachers were retained by schools, 10% migrated to different schools, and 6% left the profession every year, in rates similar to non-music teachers. Transferring music teachers migrated primarily to different school districts in the same state. One year after leaving the profession, former teachers were attending college (28%), retired (23%), out of teaching (21%), in education but not as a teacher (14%), or working as a homemaker (12%). Approximately one third of former music teachers planned to return to teaching within 5 years, and an additional quarter planned to return after 5 or more years.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".