The Effect of Principal’s Distributed Leadership Practice on Students’ Academic Achievement: A Systematic Review of the Literature
Bibliographic record
Abstract
Distributed leadership as a new scenario of educational leadership has become a popular topic in the contemporary world. Many notable researchers and members of the discourse community have contributed significantly to its development. However, little attention has paid to its effectiveness. Hence, the main purpose of this article is to analyze the existing evidence for the effect of principal’s leadership practice on students’ academic achievement from a distributed perspective. To do so, a systematic search of academic databases was conducted and 68 references spanning from the year 2001 to 2018 were selected and systematically reviewed. Due consideration was given to their concepts of distributed leadership practice, principal’s role, and students’ academic achievement. The findings of this article show that distributed leadership has positive and indirect effect on students’ academic achievement and the role of principal is indispensable. Nevertheless, there is little emperical evidence, a lack of universal accepted patterns and best practices of distributed leadership which strains further investigation. On the basis of evidence currently available, it seems reasonable to recommend scholars, policy developers, and practitioners to recognize the role of principal on distributed leadership and its best 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 | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".