Analyzing the composition of the editorial boards in high-impact medical ethics journals: a survey study
Bibliographic record
Abstract
BACKGROUND: The underrepresentation of scholarly works from low- and middle-income countries (LMICs) in academic literature is a documented concern, attributed partly to editorial biases. This trend, prevalent across various disciplines, has been less explored in the context of medical ethics journals. This study aimed to examine the composition of editorial board members (EBM) in high-impact medical ethics journals and to evaluate the extent of international diversity within these editorial teams. METHODS: This study incorporated an analysis of 16 high-impact medical ethics journals. Information regarding the EBM of these journals was systematically gathered and categorized based on the World Bank's country income classifications. An in-depth examination of the editorial board compositions was then conducted. RESULTS: The study identified 669 EBM across the selected journals. A predominant 89.84% (601) of these members were from high-income countries (HICs), with upper-middle-income countries contributing 7.47% (50) and lower-middle-income countries 2.69% (18). No EBM were associated with low-income countries. A regional breakdown indicated that North America was the most represented area, accounting for 48.88% (327), followed by Europe & Central Asia (27.50%, 184), East Asia & Pacific (13.45%, 90), Latin America & Caribbean (4.63%, 31), Sub-Saharan Africa (4.19%, 28), Middle East & North Africa (0.75%, 5), and South Asia (0.60%, 4). In total, these EBMs hailed from 46 different countries, with the United States representing the largest proportion (43.80%, 293), followed by the United Kingdom (13.15%, 88), Australia (7.92%, 53), Germany (6.73%, 45), and Canada (5.08%, 34). CONCLUSIONS: There is a significant lack of international representation within the EBM of high-impact medical ethics journals. The majority of editors in this field are affiliated with HICs, leading to a severe underrepresentation of LMICs within the editorial boards.
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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 | 0.078 | 0.198 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.013 |
| Insufficient payload (model declined to judge) | 0.000 | 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".