A bibliometric analysis of geographic disparities in the authorship of leading medical journals
Bibliographic record
Abstract
BACKGROUND: It has previously been reported that authors from developing countries are underrepresented in medical journals. Here, we aimed to build a comprehensive landscape of the geographical representation in medical research publications. METHODS: We collected bibliometric data of original research articles (n = 10,558) published between 2010 and 2019 in five leading medical journals and geolocated these by the institute of the corresponding authors. We introduced two simple metrics, the International Research Impact and the Domestic Self-Citation Index, to assess publishing and citing patterns by cities and countries. RESULTS: We show that only 32 countries published more than 10 publications in 10 years equaling 98.9% of all publications. English-speaking countries USA (48.2%), UK (15.9%), Canada (5.3%), and Australia (3.2%) are most represented, but with a declining trend in recent years. When normalized to citation count, 9/32 countries published ≥ 10% more than expected. In total, 85.7% of the publication excess originate from the USA and UK. We demonstrate similar geographical bias at the municipal level. Finally, we discover that journals more commonly publish studies from the country in which the journal is based and authors are more likely to cite work from their own country. CONCLUSIONS: The study reveals Anglocentric dominance, domestic preference, but increased geographical representation in recent years in medical publishing. Similar audits could mitigate possible national and regional disparities in any academic field.
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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 | 0.007 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.048 | 0.080 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".