Planning for Principal Succession: A Conceptual Framework for Research and Practice
Bibliographic record
Abstract
Many school districts struggle to recruit sufficient high-quality principals for their schools. A variety of conditions contribute to this challenge, including the retirement of the baby boom cohort and diminishing interest in administrative careers due to the expanded responsibilities of school principals. In response, districts enact a range of policies and programs explicitly aimed at identification and development of school leaders. Our study examined the actions taken by six districts drawing on the succession-planning perspective, which is common in the public and private sector management literature but less represented in education research. We found that intentional succession planning enabled districts to develop a pool of high-potential administrative candidates through integrated attention to candidate selection and development. While analyzing the effectiveness of “homegrown” leaders is beyond the scope of this inquiry, leaders in our six focal districts believed that they were able to increase the quality and effectiveness of their principals through intentional succession planning. We present a model for principal succession planning in education based on our empirical findings and on literature-based principles that can guide program design and future research.
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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 | 0.103 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.012 | 0.067 |
| Scholarly communication | 0.021 | 0.025 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".