A Case Study of High School Leadership Teams Managing Team Turnover
Bibliographic record
Abstract
School leaders and leadership teams can positively impact a school environment. The reality of persistent leadership turnover in schools, however, challenges the capacity of school leaders to sustain learning improvement and change. Turnover on leadership teams has been linked with negative impacts on student performance and can have deleterious effects on school growth and progress. Given the paucity of research on leadership team effectiveness in response to turnover, and in consideration of the potential negative impact leadership turnover can have in schools, it is important to look more closely at teams that are effectively negotiating this change. This study focused on school leadership team actions in response to turnover. The researcher utilized a thematic cross-case analysis of three senior high leadership teams in three different school divisions in Southern Alberta that had experienced turnover within the last five years. Perception surveys were used to select leadership teams who identified their leadership actions as successful. Each team member selected for the study participated in semi-structured interviews to determine which specific actions were resultant in effective leadership practice. Five elements of leadership team coherence were considered as part of the study: mission and vision, culture, trust, instructional leadership, and distributed leadership. Results of the research highlighted the importance of leadership team coherence and the establishment of trust. Additionally, it was found that the context of the school played an important role in determining what aspects of leadership were most important for team function. Considering the elements of leadership team coherence and the importance of trust and context, results of the study offer implications for current senior high leadership teams and make further recommendations for ongoing research surrounding effective actions of leadership teams.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 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".