Disruptive seeds: a scenario approach to explore power shifts in sustainability transformations
Bibliographic record
Abstract
Abstract Over the last 2 decades, it has become increasingly evident that incremental adaptation to global environmental challenges—particularly climate change—no longer suffices. To make matters worse, systemic problems such as social inequity and unsustainable use of resources prove to be persistent. These challenges call for, such is the rationale, significant and radical systemic changes that challenge incumbent structures. Remarkably, scholarship on sustainability transformations has only engaged with the role of power dynamics and shifts in a limited fashion. This paper responds to a need for methods that support the creation of imaginative transformation pathways while attending to the roles that power shifts play in transformations. To do this, we extended the “Seeds of Good Anthropocenes” approach, incorporating questions derived from scholarship on power into the methodology. Our ‘Disruptive Seeds’ approach focuses on niche practices that actively challenge unsustainable incumbent actors and institutions. We tested this novel approach in a series of participatory pilot workshops. Generally, the approach shows great potential as it facilitates explicit discussion about the way power shifts may unfold in transformations. It is a strong example of the value of mixing disciplinary perspectives to create new forms of scenario thinking—following the call for more integrated work on anticipatory governance that combines futures thinking with social and political science research into governance and power. Specifically, the questions about power shifts in transformations used in this paper to adapt the Seeds approach can also be used to adapt other future methods that similarly lack a focus on power shifts—for instance, explorative scenarios, classic back-casting approaches, and simulation gaming.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".