Effective school leadership practices supporting the Alberta Initiative for School Improvement (AISI)
Bibliographic record
Abstract
This study will reveal leadership practices that formal leaders and their followers identify as contributing to sustainable change in schools through the analysis of trends in quantitative data and synthesis of related qualitative data. Explored within the framework of the Alberta Initiative for School Improvement (AISI), this study is a timely contribution to the provincial context of public education. From provincial politics to grass roots strategies in schools, this research complements the current literature base with an informed Albertan perspective on effective leadership for continous school improvement. The interviews in this study demonstrate leadership practices that are prevalent in schools with improvement projects through Cycle 1 of AISI and into Cycle 2. These practices correlate, in varying degrees, with a model of transformational leadership. As Cycle 2 enters its third and final year, AISI has served as a catalyst for leadership strategies creating a culture of continuous improvement. Momentum is building as teachers become accustomed to using a data to show how student learning is improving. Alberta Initiative for School Improvement has undeniably impacted the responsibilities and experiences of teachers in schools and has moved educational leadership along the spectrum from traditional to transformational. considerable work has been done to engage staff in decision-making and setting priorities for improvement, resulting in the mobilization of school communities looking for ways to ensure high quality learning opportunities for all.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".