The Intellectual Evolution of Educational Leadership Research: A Combined Bibliometric and Thematic Analysis Using SciMAT
Bibliographic record
Abstract
This study aims to describe the century-long trajectory of educational leadership research (ELR), including changes over time in its main and subsidiary themes, as well as its most influential authors, papers, and journals. The study combines the bibliometric performance and science mapping analysis of 7282 articles retrieved from the Scopus and WoS databases. SciMAT software (version 1.1.04) was used to analyze changes over four sequential time periods and to exhibit the thematic evolution of the field—Period 1 (1907 to 2004), Period 2 (2005 to 2012), Period 3 (2013 to 2019), and Period 4 (2020–2023). Research during Period 1 focused on principals and included efforts to distinguish between their administrative functions and forms of ‘strong’ leadership contributing to school improvement. Period 2 included research aimed at understanding what strong principal leadership entailed, including the development and testing of more coherent models of such leadership. While instructional and transformational leadership models were prominent during Periods 1 and 2, Period 3 research invested heavily in conceptions of leadership distribution. Early research about ‘social justice leadership’ appeared during this period and eventually flourished during Period 4. While principals were an active focus through all Periods, the leadership of others gradually dominated ELR and accounted for the broader leadership theme found in all four periods. The results point to the evolutionary nature of ELR development, which eventually produced a relatively robust knowledge base. Experiences with the COVID-19 pandemic suggest that crises such as this might prompt more revolutionary orientations in the ELR field.
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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 | 0.035 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.187 | 0.227 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.005 |
| 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".