Conflicting Scientific Narratives at the Convention on Biological Diversity and Other Fora: Analysis and Contradiction in the Discussions on Dematerialization of (Plant) Genetic Resources
Bibliographic record
Abstract
This article examines the use of scientific arguments in negotiations on the status of Digital Sequence Information (DSI), focusing on the Convention on Biological Diversity (CBD), the International Treaty on Plant Genetic Resources for Food and Agriculture (ITPGRFA), and the Pandemic Influenza Preparedness Framework (PIP). DSI is a placeholder term used in negotiations on the dematerialization of genetic resources: the ability to sequence “physical” genetic resources and use this “intangible” information, which radically changes research practices. The CBD (among other instruments) establishes rules for Access to the Genetic Resource and the Fair and Equitable Sharing of Benefits from their utilization (ABS). This applies to “physical” genetic resources, but it is not clear for DSI. Indeed, different legal interpretations and political narrative are conflicting over the integration of DSI into these legal frameworks. This article explores how science is used in these negotiations, particularly in its rhetorical and epistocratic dimensions. The methodology combines an interdisciplinary approach (legal technique, philosophy of law and science) and a comparative discourse analysis: on the terminology; the inclusion of DSI in the definition of genetic material; and the inclusion of DSI in ABS systems. While scientific arguments play a crucial role in this technical issue, this article shows that scientific arguments can be used to support political positions (under the guise of objectivity and neutrality), and that this use of scientific arguments is not consistent, even contradictory (between PIP and CBD/ITPGRFA).
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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 | 0.128 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.034 | 0.112 |
| Scholarly communication | 0.035 | 0.043 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.021 | 0.021 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".