A BIBLIOMETRIC REVIEW OF RESEARCH ON PARTICIPATIVE LEADERSHIP THEORY IN EDUCATION: SCIENCE MAPPING THE LITERATURE, 1926-2023
Bibliographic record
Abstract
This study aims to undertake a comprehensive analysis addressing the extent, growth patterns, and geographical spread, of journals, authors, and articles that have demonstrated the most significant impact in terms of relevance. The methodology entails the use of visualization techniques and bibliometric analysis as quantitative tools and the adoption of an evaluative and descriptive approach to depict the research analysis. The data sourced from the reputable Scopus database have led to the identification of a total of 850 publications, encompassing various formats such as repositories, books, book chapters, journal articles, and proceedings. In the analysis of these publications, the study delves into the years 1926-2023, employing the R Studio and VOS Viewer software. The finding shows that North America and Europe are well-represented among the top contributors. The article's production over time highlights diverse trajectories among countries like Spain, the United Kingdom, the USA, Indonesia, and Canada. However, it's noteworthy that high productivity doesn't guarantee a similar level of citation impact. The relationship between productivity and relevance, is intricate, reflecting the dynamic nature of research impact in the academic domain. Furthermore, the analysis reveals a noticeable upward trend in the frequency of key categories such as leadership, human, article, education, and others over the specified period. Furthermore, the persistence of these trends from 2021 to 2023 signifies the enduring relevance and evolution of participative leadership research. The exploration of correlations with traditionally less associated dimensions reflects the field's adaptability and responsiveness to contemporary issues.
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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 | 0.017 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.152 | 0.207 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".