Catalyzing Collaboration Over Competition: A Pan-American Science Diplomacy Framework for Developing AI Solutions for Climate Change in the Americas
Bibliographic record
Abstract
As the dual crises of climate change and rapid artificial intelligence (AI) development converge, the Americas stand at a critical juncture. While AI offers transformative potential for climate mitigation and adaptation, ranging from advanced environmental monitoring to smarter disaster response, nationalistic policies and fragmented data governance threaten to undermine regional and global progress. This article proposes a Pan-American Science Diplomacy Framework designed to catalyze collaboration over competition in developing AI solutions for climate change. Drawing on comparative analyses of the United States, Canada, Brazil, and Chile, we highlight both the strengths and limitations of current national approaches to AI and climate action. We argue that harmonizing data standards, sharing infrastructure, and fostering inclusive governance, particularly by integrating Indigenous and local knowledge, are essential for equitable and effective AI-driven climate solutions. The framework centers on four pillars: infrastructure, data, talent, and governance, emphasizing the need for interoperable data protocols, pooled resources, and trust-building mechanisms. By leveraging existing regional institutions and diplomatic platforms, the Americas can model a new era of science diplomacy that accelerates innovation, bridges capacity gaps, and ensures that the benefits of AI for climate action are shared equitably across the hemisphere.
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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 | 0.017 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.004 |
| 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".