Scientific productivity of OECD countries in dermatology journals within the last 10‐year period
Bibliographic record
Abstract
BACKGROUND: Scientific productivity is closely related to gross income, population, and cultures of the countries. Every country, more or less, has a responsibility of contributing to science. MATERIALS AND METHODS: The publications, citations received, and the h-index under the category of "dermatology" in 43 journals between the years of 1999-2003 and 2004-2008 according to the ISI JCR data of 2008 were examined individually for each OECD country. RESULTS: In the journals under the category of "dermatology" between the years of 1999 and 2008, there were 89,319 publications, 76,899 of which were published by OECD countries. USA ranks first with 27,109 publications and 196,002 citations; Germany, Japan, England, and France are the other countries among the top five, respectively. Regarding the number of publications, Turkey and Korea are among the top 10 by surpassing many Northern European countries. With regard to h-index and citations, Northern European countries and Canada rank among the top 10, while Japan, Spain, Turkey, and Korea rank behind. The number of publications showed a significant correlation with the number of citations, population, gross domestic product, and h-index. CONCLUSIONS: Nearly half of all publications were performed by the European origin OECD countries, and one-third of all publications were performed by USA. Journals from Germany and France, which are published in their own language, receive fewer citations, but they contribute a lot to these countries with respect to the number of publications.
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 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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.023 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".