Assessing coverage of the monitoring framework of the Kunming-Montreal Global Biodiversity Framework and opportunities to fill gaps
Bibliographic record
Abstract
The Kunming-Montreal Global Biodiversity Framework (GBF) is the most ambitious multilateral agreement on biodiversity to date. It calls for a whole-of-government and whole-of-society approach to halt and reverse biodiversity loss worldwide. The GBF's monitoring framework lays out how Parties to the Convention on Biological Diversity are expected to report on their progress. An expert group convened by the Convention on Biological Diversity, the Ad Hoc Technical Expert Group (AHTEG) on Indicators, provided guidance on its implementation, including a gap analysis to identify the strengths and limitations of the indicators in the monitoring framework. We present the results of the AHTEG gap analysis and provide recommendations on implementing and improving monitoring of the GBF. We compare three implementation scenarios, from worst-case to best-case: (1) Parties only report on required headline and binary indicators; (2) Parties also report on all headline indicator disaggregations and (3) Parties additionally report on all optional component and complementary indicators. In each case, the monitoring framework covers (1) between 19-40%, (2) 22-41% and (3) 29-47% of the elements in the GBF's goals and targets. Even in the best-case scenario (3), no indicators are available for 12% of the GBF's elements. In practice, the coverage and thus effectiveness of the monitoring framework will depend on which indicators (required and optional) and disaggregations countries apply. Substantial investment is required to collect the necessary data to compute indicators, infer change and effectively monitor progress. We highlight important next steps to progressively improve the efficacy of the monitoring framework.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".