Three Key considerations for biodiversity conservation in multilateral agreements
Bibliographic record
Abstract
Abstract It is nearly three decades since the world recognized the need for a global multilateral treaty aiming to address accelerating biodiversity loss. However, biodiversity continues to decline at a concerning rate. Drawing on lessons from the implementation of the current strategic plan of the Convention on Biological Diversity and the 2010 Aichi Targets, we highlight three interlinked core areas, which require attention and improvement in the development of the post‐2020 Biodiversity Framework under the Convention on Biological Diversity. They are: (1) developing robust theories of change which define agreed, adaptive plans for achieving targets; (2) using models to evaluate assumptions and effectiveness of different plans and targets; and (3) identifying the common but differentiated responsibilities of different actors/states/countries within these plans. We demonstrate how future multilateral agreements must not focus only on what needs to be done but also on how it should be done, using measurable steps, which make sense at the scales at which biodiversity change happens.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".