MAPPING THE INTELLECTUAL LANDSCAPE OF VALUE-BASED EDUCATION: A BIBLIOMETRIC ANALYSIS OF RESEARCH TRENDS
Bibliographic record
Abstract
This bibliometric analysis investigates the intellectual landscape and research trends in Value-based Education (VbE), a pedagogical paradigm that emphasizes the integration of moral, ethical, and civic values within teaching and learning. Despite its growing global significance, the field remains theoretically fragmented and lacks a comprehensive mapping of scholarly development. Addressing this gap, the present study aims to systematically analyze research output, thematic evolution, and collaborative patterns within VbE scholarship. Using the Scopus database as the primary data source, a total of 611 documents were retrieved through a targeted search strategy that incorporated keywords such as “VbE,” “teaching,” and “learning.” The dataset was refined and standardized using OpenRefine, followed by in-depth visualization and network analysis using VOSviewer software. The analysis reveals that the United Kingdom, the United States, and Australia are the leading contributors in terms of volume and citations. At the same time, countries such as Sweden and Canada demonstrate strong international collaborations. Keyword co-occurrence analysis highlights dominant themes, including ethics, moral education, character development, and decision-making, indicating a multidisciplinary orientation. However, the presence of overlapping and variably defined terms such as “value education” and “values-based education” points to conceptual inconsistencies across studies. The findings also indicate a gradual increase in publications over the past two decades, reflecting a rise in scholarly interest and policy relevance. This study offers critical insights into the structure, scope, and evolution of VbE research, providing a foundational reference for future theoretical consolidation, empirical exploration, and cross-cultural collaboration. It contributes to advancing VbE as a coherent and impactful field of inquiry in the global educational discourse.
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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 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.179 | 0.226 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".