The role of values in future scenarios: what types of values underpin (un)sustainable and (un)just futures?
Bibliographic record
Abstract
Values have been recognized as critical leverage points for sustainability transformations. However, there is limited evidence unpacking which types of values are associated with specific types of sustainable and unsustainable futures, as described by future scenarios and other types of futures-related works. This paper builds on a review of 460 future scenarios, visions, and other types of futures-related works in the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services Values Assessment, synthesizing evidence from academia, private sector, governmental and non-governmental strategies, science-policy reports, and arts-based evidence, to identify the types of values of nature that underlie different archetypes of the future. The results demonstrate that futures related to dystopian scenario archetypes such as Regional Competition, Inequality, and Breakdown are mostly underpinned by deeply individualistic and materialistic values. In contrast, futures with more sustainable and just outcomes, such as Global Sustainable Development and Regional Sustainability, tend to be underpinned by a more balanced combination of plural values of nature, with a dominant focus on nature’s contribution to societal (as opposed to individual) aspects of well-being. Furthermore, the paper identifies research gaps and illustrates the key importance of acknowledging not only people’s specific values directly related to nature, such as instrumental, intrinsic, and relational human-nature values and relationships, but also broad values and worldviews that affect the interactions between nature and society, with resulting impacts on Nature's Contributions to People and opportunities for a good quality of life.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.015 | 0.033 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".