Creating space for Indigenous perspectives on access and benefit-sharing: encouraging researcher use of the Local Contexts Notices
Bibliographic record
Abstract
A recent Molecular Ecology editorial made a proactive statement of support for the “Nagoya Protocol” and the principle of benefit-sharing (Marden et al. 2020) by requiring authors to provide a “Data Accessibility and Benefit‐Sharing Statement” in their articles. Here, we encourage another step that enables Indigenous communities to provide their own definitions and aspirations for access and benefit-sharing alongside the author’s “Statement”. We invite the Molecular Ecology research community to use Biocultural-, Traditional Knowledge-, and Cultural Institution Notices to help Indigenous communities gain visibility within our research structures. Notices are one of the tools offered by the Biocultural Labels Initiative (part of the Local Contexts system) designed specifically for researchers and institutions. The Notices are highly visible, machine-readable icons that signal the Indigenous provenance of genetic resources, and rights of Indigenous communities to define the future use of genetic resources and derived benefits. The Notices invite collaboration with Indigenous communities and create spaces within our research systems for them to define the provenance, protocols, and permissions associated with genetic resources using Labels. Authors contributing to Molecular Ecology can apply Notices to their articles by providing the persistent unique identifier and an optional use-statement associated with the Notice in their “Data Accessibility and Benefit‐Sharing Statement”. In this way, our research community has an opportunity to accelerate support for the principles of the Nagoya Protocol, to alleviate concerns regarding Indigenous Data Sovereignty and equitable outcomes, and to build better relationships with Indigenous collaborators to enhance research, biodiversity, and conservation outcomes.
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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 | 0.141 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.039 |
| Scholarly communication | 0.023 | 0.030 |
| Open science | 0.004 | 0.041 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".