Global environmental change policy priorities from the Americas and opportunities to bridge the science-policy gap
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Governments and intergovernmental organizations support scientific research to produce the knowledge and tools needed to monitor and mitigate global environmental changes (GEC). However, GEC-related policy decisions are often not based on scientific evidence, and GEC research is often not based on policy-relevant questions, resulting in a science-policy gap. Assessing the GEC policy priorities of researchers and policymakers is an essential step towards closing this gap. This task was undertaken by the Inter-American Institute for Global Change Research (IAI), an intergovernmental organization pursuing science and capacity building to reach the vision of a sustainable Americas. The assessment included survey consultations, listening sessions, and an analysis of policy documents for 17 countries of the Americas. Three key findings emerged from this assessment. First, the top current priority for policymakers was Climate action, and Biodiversity and ecosystem services for researchers, with a poor alignment between the priorities of these social actors at the country level. Second, clusters of non-neighboring countries had a profile of GEC priorities more similar than clusters of neighboring countries, although there were some sub-regional clusters around particular GEC goals. Third, researchers and policymakers agreed that the lack of cross-sectoral collaboration and communication between technical and non-technical actors are important barriers. A key opportunity for policymakers was the growing funding and international cooperation for GEC, while for researchers, the growing body of evidence to inform GEC decision-making. These findings have implications for the design of research and capacity-building actions targeted to the priorities and needs of the region.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it