Psychological Individual Characteristics in School Leaders: a Scoping Review Protocol
Bibliographic record
Abstract
Available research points to certain common characteristics regarding the effectiveness of school leaders, such as their ability to manage their school and time and to create a heathy learning climate (Barkman, 2015; Daniëls et al., 2019). Many of the investigated characteristics are occupational in nature due to their specific work features and due to the institutional context of school. Therefore, the picture of school leaders’ characteristics is not complete. Taking up this desiderium, this scoping review aims to investigate what is known about psychological individual characteristics in school leaders. These characteristics may play an important role in the overall picture of school leadership, for instance if they matter in processes for selection of leaders or leadership success. The objective of this scoping review is to describe the extent and distribution of available research regarding psychological individual characteristics of school leaders according to the framework proposed by Leithwood and the Council of Ontario Directors of Education (Leithwood, 2012). Barkman, C. (2015). The characteristics of an effective school leader. BU Journal of Graduate Studies in Education, 7(1) , 14–18. https://files.eric.ed.gov/fulltext/EJ1230685.pdf Daniëls, E., Hondeghem, A., & Dochy, D. (2019). A review on leadership and leadership development in educational settings. Educational Research Review, 27(3), 110—125. https://doi.org/10.1016/j.edurev.2019.02.003 Leithwood, K. (2012). Strong Districts and Their Leadership. Council of Ontario Directors of Education. http://www.ontariodirectors.ca/downloads/strong%20districts-2.pdf
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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 | 0.091 | 0.076 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.065 | 0.013 |
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".