Dealing with the Serious Underrepresentation of Editors from Low-income Countries
Bibliographic record
Abstract
To the Editor: I have read with great interest your first editorial as the Editor-in-Chief of EPIDEMIOLOGY entitled: “If You Want to Know the End, Look at the Beginning.”1 In your editorial, you have posed an important question regarding trachoma: “I cannot fathom how in the year 2015 six million people will go blind because their access to clean water is so insufficient that they cannot wash their face.” Overall there is an extreme shortage of epidemiologic studies of this disease and of other neglected tropical diseases in low-income countries.2 Shortages of epidemiologic studies from low-income countries do not only relate to neglected tropical diseases. Evidence suggests that, at the same time that research from low-income countries contributes only a limited amount to total research production, researchers from the USA, Canada, and Western Europe hold leading positions in the fields of preventive medicine, epidemiology, and public health.3 It is disappointing that evidence further suggests that some biomedical researchers from low-income countries might believe that a biased attitude of editors from high-income countries against their work might partially explain this trend.4 Similarly, the serious underrepresentation of editors from low-income countries and its consequences have already been established for a number of health journals.5–7 This encourages me to look at the composition of EPIDEMIOLOGY’s editorial board.8 From the point of view of geographic distribution, one out of 29 members is affiliated with an institution located in China (an upper-middle-income country) and the rest are almost all affiliated with institutions in North America, Canada, and Western Europe (high-income countries). Therefore, I think it is time that EPIDEMIOLOGY, as the official journal of the International Society for Environmental Epidemiology (ISEE), take the leading role to diminish this potential/alleged bias. Appointments of an editorial board that is well balanced in terms of home institutions from diverse geographical regions across a range of national incomes might have a paramount effect on the advancement of our field and of public health. Mohsen Rezaeian Social Medicine Department Occupational Environmental Research Center Rafsanjan Medical School Rafsanjan University of Medical Sciences Rafsanjan, Iran [email protected]
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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 | 0.045 | 0.245 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".