Senior Government Officials' Educational Leadership Practices for Student Success Policy Making in the Government of Ontario
Bibliographic record
Abstract
Policy making within government is largely influenced by the leadership practices of those working within public administration. The emergence of the Ontario Ministry of Education’s Student Success Initiative in 2003 resulted in the development and implementation of a wide range of policies and programs to support student achievement and increase the provincial graduation rate. Through a case study approach, this study examines the perceptions of educational leadership practices of 14 former Ministry of Education senior staff who contributed to Student Success policy making between 2003 and 2018. Through a review of key government documents and informant interviews, this study is analyzed through the eight phases of Ontario’s Policy and Delivery Roadmap in order to understand how these staff perceive their leadership practices have impacted Student Success policy making. Findings reveal that the leadership practices that participants ascribed to in their Student Success work included collaboration and partnership building, identifying a common vision, adopting an empathetic leadership approach, placing emphasis on data collection and analysis to inform decision making and having an intrinsic motivation to address inequities, to improve the educational outcomes of students. Participants noted that meeting the needs of students as well as supporting a political agenda resulted in conflicting tensions, highlighting the importance of managing expectations, risk taking and perseverance to achieve shared goals. Findings reveal that the leadership practices identified were bounded by the organizational culture of the government. In addition, participants highlighted that sociopolitical and temporal factors as well as formal and informal leadership development opportunities, such as knowledge sharing and observing others, had an impact on their adoption of leadership practices. This study contributes to the limited knowledge base in public service leadership in government and specifically, in the field of education. In addition, this research contributes to understanding the impact of leadership practices on policy making within public administration and reveals the potential for continuous and focused leadership development of senior public servants. Based on findings from the study, recommendations are provided for policy making in government, for Ontario’s Policy and Delivery Roadmap, for leadership development and for future research.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".