How District System Leaders Support Principal Capacity Building in Ontario: A Study of the Leading Student Acheivement Initiative
Bibliographic record
Abstract
This thesis examines how district leaders, specifically Leading Student Achievement (LSA) System Leaders, support principal capacity building in the Ontario Leading Student Achievement project. The LSA project uses the strategy of networked principal capacity building to support the interaction of principals within and across schools to build professional knowledge creation and sharing. The conceptual framework for this study is based on the research on district practices that influence improved student learning. This study identifies how LSA System Leaders support networked principal capacity building through an examination of Ministry of Education expectations for LSA System Leaders; board contextual factors that influence how LSA System Leaders support networked principal capacity building; LSA System Leader personal experiences; and the use of LSA online networked learning formats. Phone interviews of a sample of LSA System Leaders confirmed their use of Ontario Leadership Framework (OLF) practices in supporting networked principal capacity building. Respondents provided evidence of how they act as implementing agents, aligning the LSA initiative with other board and Ministry of Education initiatives. During 2012-2013, LSA System Leaders noted the impact of labour unrest on their ability to collect and utilize system student achievement data to focus networked principal capacity building. The study findings confirmed the presence of resolute leadership within the guiding coalition in board committees to improve student achievement, as well as in senior administration. The study findings also confirmed that district leaders' practices to support principal capacity building evolve, respond and adapt to board and school contextual factors, and evolving principal learning needs. District leaders facilitated access to online learning formats to maximize capacity building through pre and post-session learning activities. The study findings were situated within the broader literature review to address the role of districts in supporting professional learning; the nature of district practices which support professional learning; and implications for districts leading networked principal capacity building. A further synthesis of the study findings classified district leader practices to support principal capacity building as those of focusing and aligning; resourcing and incentivizing; and using personal leadership resources
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".