Learning from experience: what the emerging global marine assessment community can learn from the social processes of other global environmental assessments
Bibliographic record
Abstract
Abstract In recent decades, international assessments of the ocean have evolved from specialized, technical evaluations of the state of the marine environment to more integrated and thematically extensive science-policy platforms. As assessment programmes such as the UN Regular Process blossom on the global stage and subsume responsibility for tracking progress on sustainable development, there is a need to consider how their processes wield influence and effectively translate knowledge into action. In the present paper, we undertake a comprehensive review of the literature on global environmental assessments (GEAs) and extract key principles that can be applied to global assessments of the marine environment. We were particularly inspired to identify how social processes could be arranged to best distill, communicate, and produce actionable knowledge. While we look to the advice of experts in the literature, we highlight specific examples from the Intergovernmental Panel on Climate Change (IPCC), Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), and the Global Environment Outlook (GEO). From this review, knowledge coproduction, multilevel collaboration, and futures thinking emerged as the dominant principles of influential and action-oriented assessments. We conclude the paper by contextualizing how these principles may be operationalized for Global Marine Assessments in the future.
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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 | 0.030 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.049 |
| Scholarly communication | 0.022 | 0.044 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".