One Elementary School’s Story: How Student-Centered Leadership is Enacted, Experienced, and Perceived to Impact Teaching Quality
Bibliographic record
Abstract
Although educational literature has provided multiple descriptions of effective leadership, many nuanced aspects of leadership practice in the context of a single school have not been well documented. This discrepancy raises questions about the understanding, interpretation, and enactment of various school leadership approaches. This qualitative single case study explored the phenomenon of leadership practice, specifically student-centered leadership, and its impact on teaching quality as it was enacted in one elementary school within a large school district in Western Canada. Data sources included semi structured interviews with four school-based leaders and four teachers, documents, artifacts, researcher field notes and a reflective journal. Analysis of these data revealed 12 findings, which were consolidated into the following three conceptual understandings: (a) student-centered district leadership; (b) multi-dimensional school leadership; and (c) collaborative, learning-focused school culture. Transferable insights from this study may serve as a model for schools and leadership teams across the school district. The study may also support school-based leaders in enacting student-centered leadership with the consistency, depth, and breadth needed to impact teaching quality and ensure positive outcomes for all students.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".