Faster, higher, stronger – together? A bibliometric analysis of author distribution in top medical education journals
Bibliographic record
Abstract
INTRODUCTION: Medical education and medical education research are growing industries that have become increasingly globalised. Recognition of the colonial foundations of medical education has led to a growing focus on issues of equity, absence and marginalisation. One area of absence that has been underexplored is that of published voices from low-income and middle-income countries. We undertook a bibliometric analysis of five top medical education journals to determine which countries were absent and which countries were represented in prestigious first and last authorship positions. METHODS: . Country of origin was identified for first and last author of each publication, and the number of publications originating from each country was counted. RESULTS: Our analysis revealed a dominance of first and last authors from five countries: USA, Canada, UK, Netherlands and Australia. Authors from these five countries had first or last authored 70% of publications. Of the 195 countries in the world, 43% (approximately 83) were not represented by a single publication. There was an increase in the percentage of publications from outside of these five countries from 23% in 2012 to 40% in 2021. CONCLUSION: The dominance of wealthy nations within spaces that claim to be international is a finding that requires attention. We draw on analogies from modern Olympic sport and our own collaborative research process to show how academic publishing continues to be a colonised space that advantages those from wealthy and English-speaking countries.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.022 | 0.256 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".