Deglobalisation? The case for diversified and decentralised global sustainability science
Bibliographic record
Abstract
Abstract Non-technical summary Recent geopolitical events remind us of the need for a resilient, global approach to sustainability science. This Commentary argues that a diverse, bottom-up approach is essential to ensure sustainability science progresses, even amid shifting political processes that threaten international collaboration and funding. Locally driven solutions that value diverse perspectives and knowledge systems are vital for resilience. By supporting community-led action, sharing ideas across regions, and recognising that sustainability means different things in different places, we can build a more flexible, inclusive, and resilient path toward achieving the Sustainable Development Goals in an uncertain world. Technical summary Recent geopolitical events provide a stark reminder of the need to build a resilient, global approach to sustainability science. Centralised, top-down models of sustainability science are likely to be vulnerable to disruptions, from pandemics to wars, that threaten progress towards the Sustainable Development Goals and jeopardise decades of collaborative advancement that are needed to support future progress. We argue that a decentralised, community-empowered model provides the foundation needed for a resilient sustainability scientific effort. By prioritising local solutions, embracing diverse knowledge systems, and fostering horizontal knowledge exchange, we can create a more resilient and adaptable framework. Sustainability science initiatives need to elevate successful local initiatives, adopt transdisciplinary approaches that include underrepresented knowledge holders, build decentralised knowledge-sharing networks, and recognise that sustainability has different meanings across cultural and geographical contexts. Social media summary Decentralised sustainability science: local, diverse, and resilient in a fractious and unpredictable world.
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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 | 0.059 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.073 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 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".