Update on Sharing and Reporting Benefits From Biodiversity Research
Bibliographic record
Abstract
implemented a policy encouraging our authors to report on non-monetary benefit sharing (Box 1) with the countries or communities providing genetic materials or traditional knowledge used in their research.This was partly a response to the widespread adoption of the Nagoya Protocol on Access and Benefit Sharing (ABS), which had made benefit-sharing a legal requirement for some of the research published in our journals.However, we also knew that non-monetary benefit-sharing was already a common practice among scientists conducting biodiversity research, but that there was little awareness about these contributions among the general public, policymakers, and influencers (Marden et al. 2021).Lastly, we felt that our policy would encourage scientists publishing in our journal to consider additional ways that they could improve benefit-sharing practices.The Nagoya Protocol is an international agreement that governs the fair and equitable sharing of benefits arising from the use of genetic resources and associated traditional knowledge (https:// www. cbd. int/ abs/ defau lt. shtml ).The Protocol was adopted in 2010 and came into force in 2014.Its main objectives were to: (1) establish rules for how researchers, companies, or institutions can access genetic resources and traditional knowledge from provider countries; (2) ensure that benefits (monetary or nonmonetary) from using these resources are shared fairly with the country, institution, or community that provided them; and (3) require countries to take measures so that users of genetic resources respect the laws and agreements of provider countries.This includes prior informed consent from the country or community providing the genetic resources and mutually agreed | MethodsWe compiled information relevant to non-monetary benefit sharing, including publication title, benefit-sharing statement, study authors, author countries, and taxa studied for all original research articles published in
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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 | 0.147 | 0.365 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.011 | 0.021 |
| Research integrity | 0.018 | 0.020 |
| Insufficient payload (model declined to judge) | 0.031 | 0.022 |
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".