Diversity in the editorial board
Bibliographic record
Abstract
Last month, I wrote about gender diversity in the editorial board (EB); this month, I'd like to address other aspects of diversity.To help provide perspective, let's look first at the composition of our authors and readers.Many years ago, when JFS began, it was primarily a US journal, with manuscripts originating primarily from North American researchers.These days, JFS is an international journal that publishes work from all over the globe.We consider any manuscript no matter its country of origin, as long as it meets the criteria set out in our aims and scope.In 2023, about 41% of our submissions came from China, with Turkey jumping up to second place this past year at 8%, and the United States ranking third at a little less than 7%.Of published articles, about 50% are typically from China, with the United States and India occupying the second and third spots, respectively.Our readership generally follows the same pattern, with China accounting for the most article downloads, followed by the United States and India.Turkey, Iran, and Brazil follow in the next spots in most categories.Despite being an international journal, however, our EB is still heavily North American.In a recent Task Force survey of how researchers perceived JFS, this fact was actually called out as a negative by one international survey participant.Specifically, it was noted that we have only one Scientific Editor (SE) who is not in North America.For Associate Editors (AEs) and EB members, we have a more diverse group, but it is still heavily North American.Across all 100 editors, 54 reside within the United States, with nine more in Canada or Mexico.After that, 15 are East Asian and 10 are in Europe, with three from South America, two each from the Middle East, India, and Africa, and one from Australia.We are clearly still heavily represented in North America, not as international as our stakeholders consider we should be.Arguably, we need more representation from around the world, particularly representing the countries that submit the most manuscripts.Based on our authorship, we should be looking for editors from China, India, Turkey, Brazil, and beyond.From an ethnicity standpoint, 41% of our editors identify as Asian/Indian, 37% as Caucasian/White, 3% as Hispanic, and 2% as Black.About 15% chose not to respond.Even
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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 | 0.034 | 0.178 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.036 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.037 | 0.021 |
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".