Framing natural assets for advancing sustainability research: translating different perspectives into actions
Bibliographic record
Abstract
Sustainability is a key challenge for humanity in the context of complex and unprecedented global changes. Future Earth, an international research initiative aiming to advance global sustainability science, has recently launched knowledge-action networks (KANs) as mechanisms for delivering its research strategy. The research initiative is currently developing a KAN on "natural assets" to facilitate and enable action-oriented research and synthesis towards natural assets sustainability. 'Natural assets' has been adopted by Future Earth as an umbrella term aiming to translate and bridge across different knowledge systems and different perspectives on peoples' relationships with nature. In this paper, we clarify the framing of Future Earth around natural assets emphasizing the recognition on pluralism and identifying the challenges of translating different visions about the role of natural assets, including via policy formulation, for local to global sustainability challenges. This understanding will be useful to develop inter-and transdisciplinary solutions for human-environmental problems by (i) embracing richer collaborative decision processes and building bridges across different perspectives; (ii) giving emphasis on the interactions between biophysical and socioeconomic drivers affecting the future trends of investments and disinvestments in natural assets; and (iii) focusing on social equity, power relationships for effective application of the natural assets approach. This understanding also intends to inform the scope of the natural asset KAN's research agenda to mobilize the translation of research into co-designed action for sustainability.
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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 | 0.041 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.007 | 0.075 |
| Scholarly communication | 0.019 | 0.034 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".