National commitments to Aichi Targets and their implications for monitoring the Kunming-Montreal Global Biodiversity Framework
Bibliographic record
Abstract
The Convention on Biological Biodiversity (CBD) exists as a major multilateral environmental agreement to safeguard biodiversity and "live in harmony with nature". To deliver it, strategies and frameworks are set out in regular agreements that are then implemented at the national scale. However, we are not on track to achieve overall goals, and frameworks so far have not been successful. This could be due to unambitious targets, low follow-through on commitments, or desired outcomes for nature not being achieved when action is taken. Here, we focus on national planning and reporting documents from a set of 30% of Parties to the CBD. We found that nearly half of the commitments mentioned in national planning documents did not appear in the Sixth National Reports and that further losses emerged due to measures reported as incomplete or ineffective. There were differences between commitments to each of the Aichi Targets, with more losses in high-profile and "institutionally challenging" Targets. Commitments from Parties in different Human Development Index categories had different outcomes among Targets, and Parties self-identifying as "megadiverse countries" had overall higher rates of reported success. Our results are important for informing the monitoring of commitment implementation in the Kunming-Montreal "global biodiversity package".
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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 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".