BRAZILIAN JOURNAL OF SPORTS MEDICINE: 10 YEARS OF INDEXATION ON THE WEB OF SCIENCE
Bibliographic record
Abstract
ABSTRACT Analyses of scientific production have attracted the interest of researchers, as they help to control the quality of what is published, identify relevant themes, and, thus enable scientific advances. Therefore, the objective of this study was to analyze the scientific production of the Brazilian Journal of Sports Medicine on the Web of Science database through a bibliometric analysis. The data were analyzed in relation to the publications, the authors, and the RBME. During the period of indexation on the Web of Science, which corresponds to the last decade, the RBME published 896 documents, most of which were original articles (801, 89.3%). The most cited keywords were exercise (117 studies), resistance training (37), and physical activity (34). Brazilian institutions had the highest number of publications, followed by Portugal and Spain, and there were also articles published in the USA, Canada, and the United Kingdom. USP and UNESP were the institutions with the greatest number of publications in the RBME over the last decade. Ten different collaboration clusters were identified, with researcher Edilson Serpeloni Cyrino standing out with the largest collaboration network. The ten years of indexation on the Web of Science reveal the consolidation of the RBME on the international scene, which has resulted in increasing views of and citations from the studies published, as well as attracting researchers from institutions of other countries to publish their work. Level of evidence II; Review.
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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 | 0.012 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.042 | 0.068 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".