Bibliographic record
Abstract
ABSTRACT Background Auditing geographical representation in medical publishing could help to mitigate possible national and regional disparities. Methods Using the Web of Science indexing database, we collected bibliometric data of original research articles published between 2010-2019 in The New England Journal of Medicine, Nature Medicine, Journal of the American Medical Association, The BMJ , and The Lancet . We studied the corresponding authors’ geolocation in regard to publication and citation count, their temporal evolution, and the journals’ and citing organizations’ nationality. Results We identified 10,558 articles. Based on the nationality of the corresponding authors’ institutes, only 32 countries published more than 10 publications in 10 years equaling to 98.9% of all publications. English-speaking countries USA (48.2%), UK (15.9%), Canada (5.3%), and Australia (3.2%) were most represented, but with a declining trend in recent years. Normalized to their accumulated citations, 9/32 countries were associated with ≥10% publication excess, of which USA (n=1,174 publications) and UK (n=410) accounted for 85.7%. Similar findings were replicated at the municipal level where all top 10 most productive cities were located in USA (n=7), UK (n=2), or Canada (n=1), and 21 out of 25 most productive cities published more articles than predicted based on their accumulated citations. Finally, we discovered that both journals published, and researchers cited more commonly research conducted in the same country. Discussion The audit revealed Anglocentric dominance, domestic preference occurring in both journals and citation selection, and increased geographical representation in recent years in medical publishing.
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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 | 0.025 | 0.186 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.027 | 0.064 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".