Understanding International Differences in Academic Author Order in General Medicine Publications
Bibliographic record
Abstract
Objectives: With increasing multinational research in general medicine, the lack of a standardized policy regarding the order of author bylines can create conflict and misunderstanding due to different practices worldwide. Methods: We examined publicly available data from websites such as Journal Citation Reports and Web of Science, focusing on original articles published in the "Medicine, General, & Internal" category in 2020. Of 169 journals in the "Medicine, General, & Internal" category, we selected the ten countries with the highest number of publications and then examined the position of the corresponding author in the author byline as an indicator of the author in charge since corresponding authors are considered to have contributed the most. Results: The top ten countries with the highest publications are the USA, China, Germany, England, Japan, France, Italy, Canada, India, and Australia. The results demonstrated that the percentage of the second author being the corresponding author was the highest in Japan compared to other countries. This percentage was 25 times higher in Japan than in the USA. Conclusions: Understanding international differences regarding author order would facilitate smoother collaboration.
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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 | 0.012 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".