Developing multiscale and integrative nature–people scenarios using the Nature Futures Framework
Bibliographic record
Abstract
Abstract Scientists have repeatedly argued that transformative, multiscale global scenarios are needed as tools in the quest to halt the decline of biodiversity and achieve sustainability goals. As a first step towards achieving this, the researchers who participated in the scenarios and models expert group of the Intergovernmental Science‐Policy Platform on Biodiversity and Ecosystem Services (IPBES) entered into an iterative, participatory process that led to the development of the Nature Futures Framework (NFF). The NFF is a heuristic tool that captures diverse, positive relationships of humans with nature in the form of a triangle. It can be used both as a boundary object for continuously opening up more plural perspectives in the creation of desirable nature scenarios and as an actionable framework for developing consistent nature scenarios across multiple scales. Here we describe the methods employed to develop the NFF and how it fits into a longer term process to create transformative, multiscale scenarios for nature. We argue that the contribution of the NFF is twofold: (a) its ability to hold a plurality of perspectives on what is desirable , which enables the development of joint goals and visions and recognizes the possible convergence and synergies of measures to achieve these visions and (b), its multiscale functionality for elaborating scenarios and models that can inform decision‐making at relevant levels, making it applicable across specific places and perspectives on nature. If humanity is to achieve its goal of a more sustainable and prosperous future rooted in a flourishing nature, it is critical to open up a space for more plural perspectives of human–nature relationships. As the global community sets out to develop new goals for biodiversity, the NFF can be used as a navigation tool helping to make diverse, desirable futures possible. A free Plain Language Summary can be found within the Supporting Information of this article.
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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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".