Understanding successful principal leadership: progress on a broken front
Bibliographic record
Abstract
Purpose To synthesize the results of the seven country reports covered in the special issue of JEA on successful school principalship. Design/methodology/approach Presents the main themes of the articles with their main implications and benefits. Examines the different models. Findings The country reports provide encouraging signs of progress in addressing this limitation. Such progress seems primarily due to the development of multiple cases, over time, within each country. This allows for ongoing refinement of ideas and data collection techniques, eventually resulting in the cross‐case reports appearing in this issue. These reports provide some indication, as well, that researchers are beginning to learn from their colleagues in other countries. Originality/value Summarizes multiple case studies of successful principal leadership in seven countries.
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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 | 0.041 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.020 | 0.028 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".