Geographical distribution of publications in the field of medical education
Bibliographic record
Abstract
BACKGROUND: The geographical distribution of publications as an indicator of the research productivity of individual countries, regions or institutions has become a field of interest. We investigated the geographical distribution of contributions to the two leading journals in the field of medical education, Academic Medicine and Medical Education. METHODS: PubMed was used to search Medline. For both journals all journal articles in each year from 1995 to 2000 were included into the study. Then the affiliation was retrieved from the affiliation field of the MEDLINE format. If this was not possible, it was obtained from the paper version of the journal. RESULTS: Academic Medicine published contributions from 25 countries between 1995 and 2000. Authors from 50 countries contributed to Medical Education in the same period of time. Authors from the USA and Canada wrote ca. 95% off all articles in Academic Medicine, whereas authors from the UK, Australia, the USA, Canada and the Netherlands were responsible for ca. 74% of all articles in Medical Education in the investigated period of time. CONCLUSIONS: While many countries contributed to both journals, only a few of them were responsible for the majority of all articles.
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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.023 | 0.029 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".