Challenges and possible solutions to creating an achievable and effective Post-2020 Global Biodiversity Framework
Bibliographic record
Abstract
ABSTRACT Global biodiversity is in crisis as a result of human activity. This biodiversity crisis has been well documented by scientists, recognized by world leaders, politicians, businesses, and citizens. Both the biodiversity and climate crises need to be addressed now. 2020 was when this change was supposed to start, with the 15th Conference of Parties (COP15) of the Convention on Biodiversity (CBD) meeting in Kunming, and the 26th Conference of Parties (COP26) of the UN Framework Convention on Climate Change meeting in Glasgow, but both meetings were postponed. COP26 was held a year late (November 2021), while COP15 was split into two, with the first part held in Kunming in October 2021, and the second part scheduled for Montreal in December 2022. This meeting in Montreal – arguably the most important in the CBDs history – must agree on the Post-2020 Global Biodiversity Framework (GBF), to reverse biodiversity loss. Failure to reach agreement in Montreal would ultimately be a failure of us all, with irreversible consequences for life on earth. Yet, with three months before the final deadline only 20% of text and two targets are agreed. This paper reviews the factors hindering progress on the agreement and suggests possible solutions.
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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 | 0.039 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.018 | 0.012 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".