Generative Leadership in Alberta
Bibliographic record
Abstract
Research is clear: School leadership quality matters. However, our knowledge of effective school leadership remains limited in at least three substantial ways. First, our understanding of school leadership effectiveness generally and school principal effectiveness specifically is limited to Western contexts, primarily North America and western European ones. Second, even in the confines of Western research and context, there has been relatively little specific focus on effectively leading low-performing schools. Third, even the conceptualization of leadership–do we mean the school principal, an administrative team, or a broader school leadership team’is a key factor in how we define and respond to the challenge of leading in low-performing schools. This book advances discussion and disseminates knowledge and global perspectives on what school leadership looks like, how it is enacted and under what circumstances, and when or where lessons might be portable.We anticipate this book having wide appeal for researchers, policymakers, and practitioners considering school leadership and how to support it effectively. The chapters suggest a noticeable level of convergence globally on how to lead low-performing schools effectively. Yet, there are clear political and culture differences that add significant gradation to how school leaders might enact best practice locally or inform policymakers and systems leaders about how to set up school leaders for success and subsequently support them. This book is one of the first that prioritizes the universality and nuance of leading low-performing schools globally.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".