Bibliographic record
Abstract
Research shows that one-quarter to one-third of teachers who leave the profession return, the majority after only a short absence. Though returning teachers can constitute a substantial share of newly hired teachers in schools each year, little is known about them, the factors associated with their decisions to return, or the schools to which they return. In this study, I use a 20-year longitudinal dataset to examine the characteristics of returning teachers as well as the personal, school, and district factors associated with their return both to the profession and to particular schools. In addition, I consider the extent to which returning teachers contribute to the systematic sorting of teachers across schools. Contrary to conventional wisdom, the loss of teachers to attrition from the profession is more likely to be permanent for smaller schools and districts outside of urban and suburban areas. In addition, both personal and job-related factors impact whether and where former teachers return, albeit differently by gender. Interestingly, personal and pecuniary factors in teaching appear to play a greater role than non-pecuniary factors on male leavers’ decisions regarding whether and where to return, whereas personal, pecuniary, and non-pecuniary factors all influence female leavers’ decisions. Finally, the study demonstrates that returning teachers on average reenter schools that are very similar in terms of student and teacher characteristics to those that they left.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".