Global knowledge–action networks at the frontlines of sustainability: Insights from five decades of science for action in <scp>UNESCO</scp>'s World Network of biosphere reserves
Bibliographic record
Abstract
Abstract Generating actionable knowledge to meet current sustainability challenges requires unprecedented collaboration across scales, geographies, cultures and knowledges. Intergovernmental programmes and place‐based knowledge–action networks have much potential to mobilize sustainability transformation. Although many research fields have benefited from research networks and comparative sites, the potential of site‐based research networks for generating knowledge at the people–nature interface has yet to be fully explored. This article presents the World Network of biosphere reserves (WNBR) of UNESCO's Man and Biosphere Programme, intentionally established for generating actionable knowledge through comparative sites envisioned as learning spaces for sustainable development. Drawing on experiences over five decades, and we offer six categories of insights. Our intent is to share the story of this network widely, distil the learnings from the network to enhance its potential to support both knowledge co‐production and collaborative action for sustainability and inform wider efforts to establish place‐based sustainability networks aimed at improving human–environment relations through knowledge and action. The WNBR has generated insights on the challenges of creating and supporting an international and inter‐governmental sustainability network to generate and mobilize place‐based interdisciplinary knowledge in the long term. Despite the challenges, site‐ and place‐based research facilitated by this network has been fundamental in creating space for sustainability science, knowledge co‐production and transdisciplinary research at the human–nature interface. We share insights on pathways to the implementation of global sustainability agendas through local networks, and the role of research in supporting learning and experimentation in local sites as they work to adapt global sustainability goals. Research in the WNBR has generated deeper understanding on social–ecological complexity and resilience in place‐based sustainability initiatives, and how collaborative platforms might facilitate collective action across landscapes. The network continues to offer a fundamental learning space on operationalizing pluralistic approaches to biodiversity conservation, for example, through its focus on biocultural diversity, offering a key opportunity for the implementation of the post‐2020 Global Biodiversity Framework. We conclude by arguing that WNBR, and similar place‐based knowledge–action networks, can support interdisciplinary and transdisciplinary research related to human–nature relationships and provide opportunities for comparative research that may yield more explanatory power than individual case studies. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.035 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".