Leveraging capacity for transformative sustainability science: a theory of change from the Future Earth Pathways Initiative
Bibliographic record
Abstract
Abstract Non-technical summary To address increasingly pressing social–environmental challenges, the transformative strand of sustainability science seeks to move beyond a descriptive-analytical stance in order to explore and contribute to the implementation of radical alternatives to dominant and unsustainable paradigms, norms, and values. However, in many cases, academia is not currently structured to support and reward inter-/trans-disciplinary and transformative endeavors. This paper introduces a theory of change for the Future Earth Pathways Initiative, and similar initiatives, to help leverage the capacity of sustainability scientists to engage in transformative research. Technical summary The increasing body of descriptive-analytical knowledge produced by sustainability science over the last two decades has largely failed to trigger the transformation of policies, norms, and behaviors it was aiming to inform. The emergent transformative strand of sustainability science is a proactive alternative approach seeking to play an active role in processes of societal change by developing knowledge about options, solutions, and pathways, and by participating in their implementation. In principle, scientists can enhance their contribution to more sustainable futures by engaging in transformative research. However, a lack of skills and competencies, relatively unmatured transformative methods and concepts, and an institutional landscape still geared toward disciplinary and descriptive-analytical research, still hinders the sustainability science community from engaging more widely in transformative research. In this paper, the Future Earth Pathways Initiative introduces a theory of change (ToC) for increasing the capacity of sustainability scientists to engage in this type of research. This ToC ultimately aims to build a growing community of practitioners engaged in transformative research, to advance concepts, methods, and paradigms to foster ‘fit-for-purpose transformative research’, and to shape institutions to nurture transformative research-friendly contexts. Social media summary What would a theory of change for leveraging the transformative capacity of sustainability science look like?
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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 | 0.020 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.054 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".