The new planning, reporting, monitoring and review mechanism under the Kunming-Montreal Global Biodiversity Framework and its implication for Japan
Bibliographic record
Abstract
The Kunming-Montreal Global Biodiversity Framework (KMGBF) of the Convention on Biological Diversity (CBD) is a comprehensive framework that includes mechanisms to enhance transparency in policy implementation. This paper outlines the enhanced planning, monitoring, reporting and review mechanism (so-called “PDCA cycle”) and considers its possible impact on Japan's national biodiversity strategies and action plans, implementation of policies, as well as domestic stakeholders. First, it was decided to standardise the content of national biodiversity strategies and action plans and national reports in line with the guidance documents. Parties will be required to plan and report their biodiversity policies bearing in mind the indicators for the global targets, by which transparency and comparability of national efforts will be strengthened. In addition, global reviews will be held in conjunction with the 17th and 19th Conferences of the Parties to the CBD (COP17 and COP19) to check the progress of the KMGBF, and each country can improve its own national strategy based on the reviews. With these new elements added, a PDCA cycle for biodiversity policy has been established internationally to a certain degree. In the future, expectations for various data and research ranging from ecology to economics are likely to increase both domestically and globally since they can contribute to understanding the progress and outcomes of biodiversity policies in an objective and comparable manner. Non-state actors such as private companies and researchers might be able to promote their activities if they are consistent with global targets. At the same time, however, there is also a risk that activities that cannot be reported using the indicators of KMGBF are less likely to be evaluated. Therefore, policies and services that will develop the capacity of all actors to plan, monitor and report in line with the KMGBF would be needed.
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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 | 0.135 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".