Seeds of good anthropocenes: developing sustainability scenarios for Northern Europe
Bibliographic record
Abstract
Scenario development helps people think about a broad variety of possible futures; however, the global environmental change community has thus far developed few positive scenarios for the future of the planet and humanity. Those that have been developed tend to focus on the role of a few common, large-scale external drivers, such as technology or environmental policy, even though pathways of positive change are often driven by surprising or bottom-up initiatives that most scenarios assume are unchanging. We describe an approach, pioneered in Southern Africa and tested here in a new context in Northern Europe, to developing scenarios using existing bottom-up transformative initiatives to examine plausible transitions towards positive, sustainable futures. By starting from existing, but marginal initiatives, as well as current trends, we were able to identify system characteristics that may play a key role in sustainability transitions (e.g., gender issues, inequity, governance, behavioral change) that are currently under-explored in global environmental scenarios. We suggest that this approach could be applied in other places to experiment further with the methodology and its potential applications, and to explore what transitions to desirables futures might be like in different places.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".