Non-White scientists appear on fewer editorial boards, spend more time under review, and receive fewer citations
Bibliographic record
Abstract
Disparities continue to pose major challenges in various aspects of science. One such aspect is editorial board composition, which has been shown to exhibit racial and geographical disparities. However, the literature on this subject lacks longitudinal studies quantifying the degree to which the racial composition of editors reflects that of scientists. Other aspects that may exhibit racial disparities include the time spent between the submission and acceptance of a manuscript and the number of citations a paper receives relative to textually similar papers, but these have not been studied to date. To fill this gap, we compile a dataset of 1,000,000 papers published between 2001 and 2020 by six publishers, while identifying the handling editor of each paper. Using this dataset, we show that most countries in Asia, Africa, and South America (where the majority of the population is ethnically non-White) have fewer editors than would be expected based on their share of authorship. Focusing on US-based scientists reveals Black as the most underrepresented race. In terms of acceptance delay, we find, again, that papers from Asia, Africa, and South America spend more time compared to other papers published in the same journal and the same year. Regression analysis of US-based papers reveals that Black authors suffer from the greatest delay. Finally, by analyzing citation rates of US-based papers, we find that Black and Hispanic scientists receive significantly fewer citations compared to White ones doing similar research. Taken together, these findings highlight significant challenges facing non-White scientists.
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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 | 0.011 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.020 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.013 |
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".