The Role of PRINCIPAL LEADERSHIP in Improving STUDENT ACHIEVEMENT
Bibliographic record
Abstract
(2004) make two important claims. First, “leadership is second only to classroom instruction among all school-related factors that contribute to what students learn at school ” (p. 7). Second, “leadership effects are usually largest where and when they are needed most ” (p. 7). Without a powerful leader, troubled schools are unlikely to be turned around. The authors stress that “many other factors may contribute to such turnarounds, but leadership is the catalyst ” (p. 7). The review, commissioned by the Wallace Foundation, is the first step in a five-year, 180-school study of the links between student achievement and educational leadership practices. The planned study is a joint effort of the Ontario Institute for Studies in Education at the University of Toronto and the University of Minnesota’s Center for Applied Research and Educational Improvement. The foundation’s first report could be released as early as November. This month’s newsletter summarizes what the review reveals about the basics of successful education leadership and offers practical suggestions for their implementation. SCHOOL AND DISTRICT LEADERSHIP has been the focus of intense scrutiny in recent years as researchers try to define not only the qualities of effective leadership but the impact of leadership on the operation of schools, and even on student achievement. A recently published literature review titled How Leadership Influences Student Learning contributes to this growing body of knowledge by examining the links between student achievement and educational leadership practices.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".