<i>BioScience</i>, a Global Biodiversity Journal, Seeks a Globally Diverse Editorial Board
Bibliographic record
Abstract
Editor in Chief This month’s BioScience issue has a typically globally diverse authorship. About half of the authors come from various European countries and the other half from North America, mostly the United States. We often have research groups reporting from Oceania, South America, and less so from Africa and Asia. Although it is reassuring that, month in and month out, we publish studies on the status of biodiversity from around the world, we also know that the journal mirrors the Global South’s underrepresentation in terms of the assessment of species conservation status and research in both fundamental and applied areas of the biodiversity sciences, including education, to more effectively inform the public of the importance of biodiversity for human well-being. Although we do have incredibly thoughtful members of the Editorial Board from the underrepresented regions, we need more from these areas. The Kunming–Montreal Global Diversity Framework has 196 countries as signatories. Therefore, it is also clear to us that we need a more globally diverse Editorial Board to provide the nuanced perspectives of biodiversity issues and related policies where so much of the world’s biota can still be found. I am happy to report that we have nearly equitable gender representation on the Editorial Board, and we would like to maintain this proportion as we expand our Editorial Board to the Global South. We realize that much competent science on biodiversity issues related to the Global Science comes from people who reside elsewhere, and we welcome that. If you are a serious reader of BioScience, have reviewed for the journal on multiple occasions, or have contributed to a BioScience article, we welcome your expression of interest or nomination of someone else to serve on the Editorial Board. Thank you!
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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 | 0.020 | 0.101 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.037 | 0.009 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.040 | 0.041 |
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".