Ensuring a Post-COVID Economic Agenda Tackles Global Biodiversity Loss
Bibliographic record
Abstract
The COVID-19 pandemic has caused dramatic and unprecedented impacts on both global health and economies. Many governments are now proposing recovery packages to get back to normal, but the 2019 Intergovernmental Science-Policy Platform for Biodiversity and Ecosystem Services Global Assessment indicated that business as usual has created widespread ecosystem degradation. Therefore, a post-COVID world needs to tackle the economic drivers that create ecological disruptions. In this perspective, we discuss a number of tools across a range of actors for both short-term stimulus measures and longer-term revamping of global, national, and local economies that take biodiversity into account. These include measures to shift away from activities that damage biodiversity and toward those supporting ecosystem resilience, including through incentives, regulations, fiscal policy, and employment programs. By treating the crisis as an opportunity to reset the global economy, we have a chance to reverse decades of biodiversity and ecosystem losses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.012 | 0.018 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".