Global collaborative implementation of Kunming-Montreal Global Biodiversity Framework: An analysis of challenge and solutions based on the SFIC model
Bibliographic record
Abstract
Background & Aims: After the Conference of the Parties to the Convention on Biological Diversity (CBD), the Kunming-Montreal Global Biodiversity Framework (GBF) was implemented to address global biodiversity priorities. This paper brings in a holistic, systematic thinking path based on the SFIC model to research the challenges faced in the implementation of the Kunming-Montreal GBF, and puts forward corresponding policy priorities that offer suggestions to policy-makers on implementation. Methods: This paper identifies documents related to Kunming-Montreal GBF, Aichi Targets, CBD, United Nations Environment Programme (UNEP), as well as global biodiversity governance and analyzes their contents. Results: Our results indicate that the implementation of Kunming-Montreal GBF needs global collaborative cooperation instead of acting separately and identifies a lack of holistic analysis in current research efforts. We then combine elements in the SFIC model with data on biodiversity governance, and analyze the implementation challenges. These challenges include basic differences between developing and developed countries, cooperating relationships,
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".