MAPPING THE LANDSCAPE OF SCIENTIST-SCHOOL PARTNERSHIPS IN STEM EDUCATION: A BIBLIOMETRIC ANALYSIS
Bibliographic record
Abstract
Scientist-School Partnerships (SSP) in Science, Technology, Engineering, and Mathematics (STEM) education have garnered increasing attention as a transformative model. It seeks to enrich science teaching, foster authentic inquiry, and bridge formal education with scientific practice. However, despite the growing implementation of such collaborations, a comprehensive overview of global research trends, influential contributors, and thematic directions remains limited. This bibliometric analysis aims to map the scholarly landscape of SSP in STEM education, identifying research patterns, dominant countries, institutions, and evolving themes. Using a structured search strategy in the Scopus database, we applied a keyword combination of "scientist," "school," "partnership," and "STEM education," yielding a total of 1,054 documents. Data were refined and standardized using OpenRefine to ensure consistency and eliminate redundancies. Quantitative analyses were performed using Scopus Analyzer, while network visualizations and co-occurrence maps were generated with VOSviewer to identify keyword clusters and author collaboration networks. Results indicate that the United States (US) leads in research output and citation impact, followed by the United Kingdom (UK), Canada, and Australia. Thematic mapping reveals core research domains centered on inquiry-based learning, science communication, professional development, and interdisciplinary curriculum integration. Furthermore, the collaboration patterns demonstrate a concentration of contributions from high-income countries, with emerging participation from Latin America and Asia. This study contributes a systematic bibliometric perspective to the literature, offering insights into current knowledge structures and gaps. In addition, the findings underscore the significance of expanding inclusive international collaborations. This is particularly relevant in supporting underrepresented regions and suggests future research directions focusing on sustainable models of scientist-school engagement that are contextually and culturally relevant.
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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 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.166 | 0.248 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| 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".