Seeds of good anthropocenes: developing sustainability scenarios for Northern Europe
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it