Servant Leadership in educational contexts: A bibliometric analysis to guide management in the framework of SDG 4
Bibliographic record
Abstract
Servant leadership is a leadership approach that focuses fundamentally on service to others. In educational contexts, servant leadership has become an attractive topic for research. The main objective of this research was to analyze the last 11 years of scientific production from the Scopus database on servant leadership in educational settings (2013-2023). 48 publications analyzed using bibliometric indicators were taken as a data source. The analysis includes visualization, bibliographic coupling, co-occurrence, and co-authorship analysis. The results of the study show that collaborative research networks between countries around the world were examined, with the United States emerging as the dominant contributor, followed by Indonesia, India, China, and Peru, with the period 2020 and 2022 being those with the highest scientific production, also validating that K. Dahleez, KF Latif, and F. Marimon were the most influential contributors in scientific production and citations; however, A. Abbas has made a greater contribution to Sustainable Development Goal 4, Quality education. The major legacy has focused on two main thematic areas, the Social Sciences and Business, Management and Accounting. The analysis of the main keywords revealed a deep research interest, highlighting the extension and diversity of the concepts of servant leadership, higher education, education, teachers, knowledge hiding, learning, leadership and life satisfaction. This study highlights the importance of international cooperation in the field of cultural research and the need for countries to develop research capital to address knowledge gaps. These findings reveal research trends for future studies, influential authors, and co-authorships across countries, helping researchers identify gaps and future directions. The findings may facilitate the development and use of servant leadership in educational settings to improve outcomes as a sector.
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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 | 0.015 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.155 | 0.170 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".