Transformative foresight for diverse futures: the Seeds of Good Anthropocenes initiative
Bibliographic record
Abstract
Abstract Motivation Foresight methods are increasingly recognized as essential for decision‐making in complex environments, particularly within development and research settings. As foresight methods continue to gain prominence for decision‐making, their application in these settings grows. Funders and policy‐makers can benefit from the experience of transformative foresight practitioners and researchers who are skilled in designing novel ways to envision alternative and diverse development futures. Purpose The Seeds of Good Anthropocenes (SoGA) initiative has experimented with transformative foresight since its inception in 2016. We position SoGA within the framework of Minkkinen et al. (2019); we present its transformative capacity through participatory visioning; and we explore how foresight methods can shape strategic development options. Approach and methods We draw lessons from how SoGA, used extensively in various contexts around the world, has introduced experimental transformative foresight to deal with diversity and complexity. We describe the transformative foresight processes in detail. Findings SoGA exemplifies how transformative foresight can support policy and change initiatives by providing participants, planners, and decision‐makers with opportunities to reinforce the collaborative and transformative objectives of their policy and convening practices. Such engagement not only deepens the strategic impact of policies, it also encourages a more inclusive and participatory approach to policy development, aligning with broader goals for sustainable and impactful change.
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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 | 0.107 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".