Governance of a global genetic resource commons for non-commercial research: A case-study of the DNA barcode commons
Bibliographic record
Abstract
Life sciences research that uses genetic resources is increasingly collaborative and global, yet collective action remains a significant barrier to the creation and management of shared research resources. These resources include sequence data and associated metadata, and biological samples, and can be understood as a type of knowledge commons. Collective action by stakeholders to create and use knowledge commons for research has potential benefits for all involved, including minimizing costs and sharing risks, but there are gaps in our understanding of how institutional arrangements may promote such collective action in the context of global genetic resources. We address this research gap by examining the attributes of an exemplar global knowledge commons: The DNA barcode commons. DNA barcodes are short, standardized gene regions that can be used to inexpensively identify unknown specimens, and proponents have led international efforts to make DNA barcodes a standard species identification tool. Our research examined if and how attributes of the DNA barcode commons, including governance of DNA barcode resources and management of infrastructure, facilitate global participation in DNA barcoding efforts. Our data sources included key informant interviews, organizational documents, scientific outputs of the DNA barcoding community, and DNA barcode record submissions. Our research suggested that the goal of creating a globally inclusive DNA barcode commons is partially impeded by the assumption that scientific norms and expectations held by researchers in high income countries are universal. We found scientific norms are informed by a complex history of resource misappropriation and mistrust between stakeholders. DNA barcode organizations can mitigate the challenges caused by its global membership through creating more inclusive governance structures, developing norms for the community are specific to the context of DNA barcoding, and through increasing awareness and knowledge of pertinent legal frameworks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.020 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".