An Examination of Teacher Leaders and a Shared Leadership Approach: Contributions to System Improvement in a School District
Bibliographic record
Abstract
There is an emerging body of evidence that recognises the significant role informal teacher leaders play in the pursuit of school and system improvement. This article reports the results of a multi-phase, design based research study conducted with teacher leaders, assistant principals and principals who participated in a two-year design-based professional learning initiative with the goal of building capacity for instructional leadership and system improvement. The question guiding the study was: In what ways do teacher leaders contribute to system improvement? Three dimensions of focus associated with high-performing systems emerged from the data in connection to the investment in professional capital that contributed to system improvement: (a) enhancing the quality of teaching and learning for school and district improvement, (b) preserving continuous design-based professional learning opportunities, and (c) ensuring opportunities for collaborative learning alongside colleagues and the development of a network of teacher leaders with a shared purpose. Teacher leaders are informal leaders and important members of an instructional leadership team contributing to school and district improvement.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".