Lessons from COP15 on effective scientific engagement in biodiversity policy processes
Bibliographic record
Abstract
The Kunming-Montreal Global Biodiversity Framework was adopted by parties to the Convention on Biological Diversity in December 2022. The aftermath of these negotiations provides an opportunity to draw lessons as to how ecological and evolutionary science can more effectively inform policy. We examined key challenges that limit effective engagement by scientists in the biodiversity policy process, drawing parallels with analogous challenges within global climate negotiations. Biodiversity is multifaceted, yet represents only one framing for nature's contributions to people, complicating the nexus between evidence and values in development of the framework's targets. Processes generating biodiversity and driving its loss are multiscalar, challenging development of an evidence base for globally standardized targets. We illustrated these challenges by contrasting development of 2 key elements of the framework. The genetic diversity element of the framework's target 4 is directly related to the framework's primary goals, but its complexity required development of novel engagement skills. The target for protected areas was easily communicated but more indirectly related to biodiversity outcomes; evidence from ecological and social science was essential to communicating the context and limitations of this relationship. Scientists can strengthen the effectiveness of global agreements and address challenges arising from complexity, scaling, capacity limitations, and the interplay of science and values, if they can prioritize communication, consensus-building, and networking skills and engage throughout the process, from development of an evidence base to implementation.
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 | 0.133 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.074 |
| Scholarly communication | 0.033 | 0.024 |
| Open science | 0.005 | 0.038 |
| Research integrity | 0.020 | 0.017 |
| Insufficient payload (model declined to judge) | 0.020 | 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".