A bibliometric analysis of Tzu Chi Foundation’s research publications using the web of science from 1990 to 2023
Bibliographic record
Abstract
Background: The Tzu Chi Foundation, a prominent Buddhist non-profit organization, has significantly contributed to global humanitarian efforts and academic research. However, its scholarly output has not been comprehensively analyzed. Aim of the study: This bibliometric study aimed to analyze the research publications of individuals affiliated with the Tzu Chi academic community, indexed in the Web of Science (WoS) from 1991 to 2023. Material and methods: Data were extracted from the Science Citation Index Expanded edition of the WoS Core Collection. Publications with authors affiliated with Tzu Chi institutions were identified using relevant keywords. Only original articles were included. Bibliometric indicators were assessed using Bibliometrix 4.1 and VOSviewer 1.6.20. Results: A total of 9,510 original articles were published by Tzu Chi affiliates between 1991 and 2023, showing an annual growth rate of 18.7%. The most frequent subject categories were “general and internal medicine”, “pharmacology and pharmacy”, “oncology”, and “biochemistry and molecular biology”. PLoS One was the top published journal. Keyword analysis highlighted apoptosis, inflammation, and stroke as prominent research topics, with emerging areas such as immune checkpoint inhibitors and COVID-19. Conclusions: This bibliometric study provided an overview of the Tzu Chi Foundation's scholarly contributions from 1991 to 2023, showing significant growth in research output and the diversification of research topics. Future efforts should focus on expanding unique research areas such as the Silent Mentor Program, stem cell research and precision medicine, and vegetarian research to enhance Tzu Chi’s global research impact, improve patient care, and foster a compassionate and effective healthcare system.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.150 | 0.214 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".