Recruitment, employment, retention and the minority teacher shortage
Bibliographic record
Abstract
This study examines and compares the recruitment, employment, and retention of minority and nonminority school teachers over the quarter century from the late 1980s to 2013. Our objective is to empirically ground the ongoing debate regarding minority teacher shortages and changes in the minority teaching force. The data we analyze are from the National Center for Education Statistics’ nationally representative Schools and Staffing Survey (SASS) and its longitudinal supplement, the Teacher Follow-up Survey (TFS). Our data analyses document the persistence of a gap between the percentage of minority students and the percentage of minority teachers in the US. But the data also show that this gap is not due to a failure to recruit new minority teachers. In the two decades since the late 1980s, the number of minority teachers almost doubled, outpacing growth in both the number of White teachers and the number of minority students. Minority teachers are also overwhelmingly employed in public schools serving high-poverty, high-minority and urban communities. Hence, the data suggest that widespread efforts over the past several decades to recruit more minority teachers and employ them in disadvantaged schools have been very successful. But, these efforts have also been undermined because minority teachers have significantly higher turnover than White teachers and this is strongly tied to poor working conditions in their schools.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".