Involving citizens in monitoring the Kunming–Montreal Global Biodiversity Framework
Bibliographic record
Abstract
Abstract The Kunming–Montreal Global Biodiversity Framework (GBF) and its monitoring framework aims to reverse the decline of nature. The GBF tasks governments to report progress towards 23 targets and four goals but also “invites Parties and relevant organizations to support community-based monitoring and information systems and citizen science” to improve information for decision-making and build support for conservation efforts throughout society. We assessed how Indigenous Peoples, local communities and citizen scientists and professional scientists can help monitor the GBF. Of the 365 indicators of the GBF monitoring framework, 110 (30%) can involve Indigenous Peoples, local communities and citizen scientists in community-based monitoring programmes, 185 (51%) could benefit from citizen involvement in data collection and 180 (49%) require scientists and governmental statistical organizations. A smaller proportion of indicators for GBF targets are amenable to citizen monitoring than for the previous Aichi targets or other multilateral environment agreements—largely because 196 GBF indicators are analytically complex (54%) and 175 require legislative overview (48%). Greater involvement of citizens in the GBF would increase societal engagement in international agreements, harness knowledge from those living close to nature to fill data gaps and enhance local to national decision-making based on improved information, leading to better conservation actions.
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 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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".