Scientometrics analysis on scientific production about meditation in medical journals
Bibliographic record
Abstract
The general objective of this study was to analyze the insertion of the theme Meditation at the core in scientific journals of the medical area, referring to the six countries (United States, United Kingdom, (SCOPUS) and England (Web of Science); India, Australia and Canada) that lead the ranking of that scientific production is indexed in the Web of Science and SCOPUS databases. This is a scientometric study, descriptive, with a quantitative approach, in the period from 2009 to 2018.The search analyzed the core in scientific journals with regard to the impact, origin, the subject category and the identification of the 15 titles that most published about the theme. The result of the research showed that the core consists of journals classified in different subjects in the medical field, in both databases. The specialties they highlighted were: Neurology (Web of Science) and Psychiatry, (SCOPUS). In reference to the impact, it was found that the majority of the titles are high impact and from countries in North America and Europe. This study can contribute to reveal patterns of behavior through metric studies, concerning to the formal channel used by that scientifical community to publish your search results.
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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 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.103 | 0.159 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".