Assessing Publication Productivity of the Top 10 Countries Across Medical Specialties: Prolific Versus Prestigious Journals
Bibliographic record
Abstract
This study aimed to investigate publication productivity in various medical specialties in the top 10 countries with the highest number of published journal articles, considering the distinction between prolific and prestigious journals. For this study, we selected 10 specialties from the Scientific Journal Rankings (SJR) and used journals listed in both SJR and PubMed. Bibliographic details of these journals’ articles published from 2017 to 2019 were downloaded from PubMed. The results showed that various aspects of medical publication output were influenced by country characteristics such as specialty, journal type, population size, wealth, and healthcare expenditure. China showed the greatest variability in terms of specialty, as its publications in Oncology (ONCGY) were exceptionally high compared with the specialties of other countries. China’s publications in ONCGY exceeded even those of the United States in ONCGY. Furthermore, the western countries, the United Kingdom, Canada, and the United States in particular published more articles in prestigious journals than the other top 10 countries, where the East Asian countries published more articles in prolific journals than in prestigious journals.
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 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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.024 | 0.031 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".