A Holistic Evaluation of Buddhism Literature: A Bibliometric Analysis of Global Publications Related to Buddhism Between 1975 and 2017
Bibliographic record
Abstract
Although Buddhism is the fourth largest religion of the world with almost 500 million followers, to the best of our knowledge, academic literature lacks a bibliometric study investigating Buddhism documents. We used four databases provided by Web of Science; Thomson Reuters to extract the academic documents related to Buddhism and included all items published between 1975 and 2017. We generated info-maps and info-graphics showing distribution of world countries’ publication productivity and connections in bibliometric networks. A total of 25,267 articles were included and the most common document types were original articles, reviews and meeting reports (76.11, 19,38 and 3.84, respectively). English and Korean were the major languages of Buddhism literature (48.12 and 44.95%). United States of America (USA) was leading country with 4572 articles (18.81%) followed by the United Kingdom, China, Canada and Japan (3.32, 2.58, 2.1 and 2.07%, respectively). The most productive countries were Singapore, Australia, New Zealand and Taiwan (s = 19.34, 18.57, 16.42 and 15.45). We noted that six of ten most producing institutions in Buddhism literature were from the USA. No institutions from developing or least-developed countries were in the top-ten list. Researchers from the countries with large Buddhist population should be encouraged and supported to carry out more articles in Buddhist literature. © 2020, Library Philosophy and Practice. All Right Received
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.117 | 0.119 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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".