Access and Benefit-Sharing in the Age of Digital Biology
Bibliographic record
Abstract
This chapter explores whether and how genomic resources can be protected by the communities from, or countries in which they are accessed. Specifically, it asks whether the Nagoya Protocol on Access and Benefit-Sharing can be an effective mechanism to reassure communities about the sharing of gene sequencing data. These questions are of particular importance to Indigenous peoples and local communities, as many have troubling historical experiences with colonization and associated natural resource exploitation. Many Indigenous and local communities (ILCs) live in developing countries, which are particularly sensitive to access and benefit-sharing (ABS) issues. Different but equally serious challenges exist for Indigenous peoples in developed countries like Canada, Australia, New Zealand and elsewhere. Until outcomes of implementation of the Nagoya Protocol are captured, Indigenous peoples and Local Communities (IPLCs) remain in a quandary as to how to protect digitized genetic resources within their territories or under their jurisdiction. To advance our understanding of legal and regulatory options, this chapter integrates normative and positive perspectives on the mechanisms for access and benefit-sharing in the age of digital biology.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.017 | 0.028 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".