Biomedical scientific publication patterns in the Scopus database: a case study of Andalusia, Spain
Bibliographic record
Abstract
This paper characterises scientific output in biomedicine in Andalusia, and Spain as a whole, and conduct a first-time comparison to Europe- and world-wide production. The data were extracted from the Scopus database. Three families of indicators are explored to analyse research quantity, quality and collaboration. The results show an upward trend on biomedical output in Andalusia. Over 50 % was in clinical medicine, whose growth doubled the basic medicine. We found greater than nationwide specialisation in biochemistry, genetics and molecular biology, immunology and microbiology, and pharmacology, while psychology proved to be the most prominent emerging area. The publication in most cited journals together with national and international collaboration enhanced research visibility. More citable papers were published on basic than clinical medicine, and the number of citations received by the former was also larger. The higher citation rate in basic medicine may also be explained by the bigger percentage of papers published in international instead domestic journals. Hence, publication patterns would appear to affect research visibility. The methodology proposed may provide guidance for public policy makers to improve, encourage and intensify good biomedical research practice.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.028 | 0.078 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".