Promising governance approaches for reversing biodiversity loss
Bibliographic record
Abstract
This perspective sheds light on the critical importance of accounting for governance systems when attempting to reverse biodiversity degradation and loss. Contributing insights from political science, governance studies, and international relations, it aims to stimulate academic and societal discussions on the value of reforming the institutional structures, management norms, and legal rules comprising the systems governing biodiversity at local, national, regional, and global scales. We identify three especially promising reforms with the potential to reverse biodiversity loss: (1) restructuring governance systems to integrate top-down and bottom-up approaches; (2) diversifying perspectives and centering governance systems on Indigenous and traditional knowledge and practices; and (3) crafting integrated policies and tapping into synergies. We demonstrate how these governance approaches can reverse biodiversity loss while promoting long-term social and ecological resilience. Additionally, we show they can increase collaboration across scales and sectors and improve policy design, effectiveness, and coherence from local to planetary contexts.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".