African biodiversity genomics in the era of the Kunming-Montreal Global Biodiversity Framework: Ethical, Legal, and Social Perspectives
Bibliographic record
Abstract
The African BioGenome Project (AfricaBP) is a Pan-African initiative which aims to improve food systems and conservation through genomics, and ensure data sharing and benefits. The Kunming-Montreal Global Biodiversity Framework (KMGBF) is one of the frameworks of the Convention on Biological Diversity which seeks to reduce threats to biodiversity, ensure sustainable use of biodiversity as well as equitable sharing of benefits. AfricaBP’s objectives and activities are closely aligned with the goals of the KMGBF. However, implementing genomic research in the African context presents unique ethical, legal and social challenges and benefits. Here, we explore the alignment between the AfricaBP and the KMGBF, focusing on the potentials for genomics to drive biodiversity conservation and food security across Africa. We critically examine the ethical, legal, and social implications (ELSI) and related challenges associated with implementing the KMGBF. In response to these challenges, and to strengthen AfricaBP’s capacity to implement the KMGBF goals, we make specific recommendations such as, amongst others, the creation of clear policy and legal frameworks, implement transparent monitoring and reporting mechanisms, and ensure interoperability of key regulatory instruments in biodiversity conservation. We also discuss how AfricaBP integrates the theory of change in its activities to enhance the implementation of the KMGBF by strengthening biodiversity data infrastructure, creating awareness via communication and capacity-building whilst empowering local communities, promoting gender diversity in the African biodiversity genomics landscape, facilitating research and innovation by advancing ethical and legal frameworks, and understanding access and benefit-sharing and KMGBF through roundtable meetings, survey development and analysis.
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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 | 0.032 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.048 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".