The<scp>CBD</scp>Post‐2020 biodiversity framework: People's place within the rest of nature
Bibliographic record
Abstract
Abstract Recognizing two decades of failure to achieve global goals and targets, parties to the Convention on Biological Diversity are in the final phase of negotiating a Post‐2020 Global Biodiversity Framework for the conservation, sustainable use and benefit sharing of biodiversity. The framework attempts to set out pathways, goals and targets for the next decade to achieve positive biodiversity change. This perspective intends to help that framework set people firmly as part of nature, not apart from it. Despite work done so far through four meetings, new thinking and focus is still needed on ‘what’ changes must be conceptualized and implemented, and ‘how’ those changes are to be delivered. To help achieve that new thinking, as a broad range of people, many with a focus on aquatic systems, we highlight six key foci that offer potential to strengthen delivery of the framework and break the ‘business as usual’ logjam. These foci are as follows: (i) a reframing of the narrative of ‘people's relationship with the rest of nature’ and emphasize the crucial role of Indigenous Peoples and Local Communities in delivering positive biodiversity change; (ii) moving beyond a focus on species and places by prioritizing ecosystem function and resilience; (iii) supporting a diversity of top‐down and bottom‐up governance processes; (iv) embracing new technologies to make and measure progress; (v) linking business more effectively with biodiversity and (vi) leveraging the power of international agencies and programmes. Given they are linked to a greater or lesser degree, implementing these six foci together will lead to a much‐needed broadening of the framework, especially those of business and broader urban civil society, as well as those of Indigenous Peoples and Local Communities. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".