The Geopolitics of Climate Knowledge Mobilization
Bibliographic record
Abstract
Climate change and sustainability science have become more international in scope and transdisciplinary in nature, in response to growing expectations that scientific knowledge directly informs collective action and transformation. In this article, we move past idealized models of the science–policy interface to examine the social processes and geopolitical dynamics of knowledge mobilization. We argue that sociotechnical imaginaries of transdisciplinary research, deployed in parallel to “universal” regimes of evidence-based decision-making from the global North, conceal how international collaborations of scientists and societal actors actually experience knowledge mobilization, its systemic barriers, and its paths to policy action. Through ethnographic study of a transdisciplinary research program in the Americas, coupled with in-depth analysis of Colombia, we reveal divergences in how participants envision and experience knowledge mobilization and identify persistent disparities that diminish the capacity of researchers to influence decision-making and fit climate knowledge within broader neoliberal development paradigms. Results of the study point to a plurality of science–policy interface(s), each shaped by national sociotechnical imaginaries, development priorities, and local social orders. We conclude that a geopolitical approach to transdisciplinary science is necessary to understand how climate and sustainability knowledge circulates unevenly in a world marked by persistent inequality and dominance.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.011 | 0.038 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| 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".