Avoid Cherry‐Picking Targets and Embrace Holistic Conservation to Pursue the Global Biodiversity Framework
Bibliographic record
Abstract
ABSTRACT The Global Biodiversity Framework (GBF) marked a renewed commitment to addressing the global biodiversity crisis. This framework of four goals and 23 interim targets is intended to guide and accelerate conservation efforts over the next 25 years and is more ambitious than its predecessor, the Aichi 2020 targets. However, the pursuit of multilateral agreements is dependent upon national pledges, and the limited success of the Aichi targets shows that national pledges are of little worth without aligned (sub)national action. We assessed the submitted National Biodiversity Strategy and Action Plans of several member countries to determine their alignment with the bold ambition of the GBF. We find a lack of alignment between the GBF and country submissions across many targets, with the notable exception of Target 3—commonly interpreted as increasing protected area coverage to 30% by 2030. Reflecting on the submissions, recent developments, and our collective experience, we outline key considerations that could help guide future submissions and implementation strategies. We caution against cherry‐picking specific targets, highlighting that an overemphasis on Target 3 will fail to achieve the overarching vision of living in harmony with nature. This requires a more holistic and inclusive approach to conservation and a focus on the full suite of GBF targets.
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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 | 0.054 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".