Secondary School Science Department Chairs Leading Successful Change
Bibliographic record
Abstract
A foundational understanding within education leadership literature is that education leaders are expected to guide reform efforts within school systems (e.g., Darling-Hammond, 2001; Evans, 1996; Fullan, 2001). This expectation mirrors organizational development literature that describes leaders as individuals who constructively institute change within their organizations (e.g., Northouse, 2004; Havelock & Zlotolow, 1995; Yukl & Lepsinger, 2005). Although leadership and change are portrayed as codependent and recursive phenomena within both educational and organizational development literature, no scholarship has linked change models with leadership theories (Herold, Fedor, Caldwell, & Liu, 2008). This article describes a multiple case study that explored the relationship between leadership behaviors and the change process through secondary school department chair stories of change. From the analysis of these stories of successful and unsuccessful change attempts, a clearer picture emerged that illustrates how leaders with little control over decisions implement change. Findings included distinct connections between CREATER change process stages (Havelock & Zlotolow, 1995) and specific leadership behaviors related to the Leadership Grid (Blake & McCanse, 1991; Yukl, Gordon, and Tabers, 2002), as well as change-leadership patterns that differentiate successful and unsuccessful change. Based on these findings, suggestions as to how education leaders should approach change attempts within their schools are discussed.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".