Recruiting New Teachers to Urban School Districts: What Incentives Will Work?
Bibliographic record
Abstract
Many urban districts in the United States have difficulty attracting and retaining quality teachers, yet they are often the most in need of them. In response, U.S. states and districts are experimenting with financial incentives to attract and retain high-quality teachers in high-need, low-achieving, or hard-to-staff urban schools. However, relatively little is known about how effective financial incentives are to recruit new teachers to high-need urban schools. This research explores factors that are important to the job choices of teachers in training. Focus groups were held with students at three universities, and a policy-capturing study was done using 64 job scenarios representing various levels of pay and working conditions. Focus group results suggested that: a) many pre-service teachers, even relatively late in their preparation, are not committed to a particular district and are willing to consider many possibilities, including high need schools; b) although pay and benefits were attractive to the students, loan forgiveness and subsidies for further education were also attractive; and c) small increments of additional salary did not appear as important or attractive as other job characteristics. The policy-capturing study showed that working conditions factors, especially principal support, had more influence on simulated job choice than pay level, implying that money might be better spent to attract, retain, or train better principals than to provide higher beginning salaries to teachers in schools with high-poverty or a high proportion of students of color.
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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 | 0.032 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".