THE EVOLUTION OF INSTRUCTIONAL LEADERSHIP: A 10-YEAR BIBLIOMETRIC PERSPECTIVE
Bibliographic record
Abstract
The study aimed to examine past study publication trends for instructional leadership over 10 years (2014 - 2024). Bibliometric analysis was conducted through the searching method on the Scopus database. The search string for the Scopus database was conducted systematically. A total of 2094 published articles were analysed using Microsoft Excel, Scopus Analyser and VOSviewer. Our analysis employed advanced bibliometric techniques including annual publication trends, greatest number of articles, most popular subject area, top ten authors, most co-authorship influential countries, collaboration networks based on the co-authorship and popular keywords. The analysis revealed that the study of instructional leadership displays an increasing trend annually. This bibliometric analysis identifies Hallinger, P. and Shaked, H. as key contributors to the instructional leadership literature, authoring 37.98% of the publications and significantly influencing the development of foundational theories. The study also reveals a predominant focus on social sciences which constitute 63.54% of the research, thereby linking instructional leadership with educational policy and management. Additionally, extensive international collaborations are evident, particularly among scholars from the United States, United Kingdom, Australia, China and Canada. Commonly cited keywords such as transformational leadership, school improvement and student achievement underscore their importance in ongoing instructional leadership discussions. This study not only maps the intellectual territory but also illustrates the evolving focus areas and collaborative patterns, providing valuable insights for researchers and policymakers aiming to enhance instructional leadership frameworks globally.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.127 | 0.190 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.002 |
| 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".