Mapping the scholarly landscape: a bibliometric exploration of school head leadership competency
Bibliographic record
Abstract
This bibliometric study examines the trends and contributions in school head leadership competencies from 2015 to 2024, using data from Scopus and employing VOSviewer. The research aims to provide a comprehensive overview of the scholarly literature on leadership competencies in the range of a school head. The methodology involves a thorough bibliometric process, including the organization, coordination, and analysis of bibliographic data from peer-reviewed academic journals. The specific methods used to define the research area are mapping of important contributors and co-authorship patterns, document co-citation analysis, and keyword frequency analysis. Preliminary results indicate a peak in publications up to 2023, with a notable decline in 2024. The study highlights significant international collaborations, with the United States at the core of a global network involving countries like Canada, Australia, and Turkey. Keywords such as "transformational leadership," "equity," and "school climate" are prominent, reflecting a broad approach to exploring effective leadership. In conclusion, the field of school head leadership competencies is dynamic, driven by global collaboration and evolving educational challenges. The recent decline in publications signals a need for new research directions. Future studies should explore unexplored areas and integrate technological advancements to enhance school head leadership competencies effectively.
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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 | 0.014 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.207 | 0.247 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".