Enabling transformative economic change in the post‐2020 biodiversity agenda
Bibliographic record
Abstract
Abstract The COVID‐19 pandemic, its impact on the global economy, and current delays in the negotiation of the post‐2020 global biodiversity agenda of the Convention on Biological Diversity heighten the urgency to build back better for biodiversity, sustainability, and well‐being. In 2019, the Intergovernmental Science‐Policy Platform on Biodiversity and Ecosystem Services (IPBES) concluded that addressing biodiversity loss requires a transformative change of the global economic system. Drawing on the IPBES findings, this policy perspective discusses actions in four priority areas to inform the post‐2020 agenda: (1) Increasing funding for conservation; (2) redirecting incentives for sustainability; (3) creating an enabling regulatory environment; and (4) reforming metrics to assess biodiversity impacts and progress toward sustainable and just goals. As the COVID‐19 pandemic has made clear, and the negotiations for the post‐2020 agenda have emphasized, governments are indispensable in guiding economic systems and must take an active role in transformations, along with businesses and civil society. These key actors must work together to implement actions that combine short‐term impacts with structural change to shift economic systems away from a fixation with growth toward human and ecological well‐being. The four priority areas discussed here provide opportunities for the post‐2020 agenda to do so.
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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 | 0.026 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.017 | 0.011 |
| Insufficient payload (model declined to judge) | 0.017 | 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".