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Record W3083797720

A Holistic Evaluation of Buddhism Literature: A Bibliometric Analysis of Global Publications Related to Buddhism between 1975 and 2017

2020· article· en· W3083797720 on OpenAlexaboutno aff
Ghouse Modin Nabeesab Mamdapur, Engin Şenel

Bibliographic record

VenueLincoln (University of Nebraska) · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicIndian and Buddhist Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBuddhismSociologyLibrary sciencePhilosophyComputer scienceTheology
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.656
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0090.025
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.108
GPT teacher head0.294
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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