Biomedical scientific publication patterns in the Scopus database: a case study of Andalusia, Spain
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.017 | 0.074 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it