Transformative foresight for diverse futures: the Seeds of Good Anthropocenes initiative
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
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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