Transformative capacities for navigating system change: a framework for sustainability research and practice
Bibliographic record
Abstract
In the face of climate change and other ecological pressures, there is urgent need to transform human systems and their society-nature relationships. However, there is a gap between transformative ambitions and our ability to enable transformative change. The relationship between sustainability transformations in practice and the transformative capacities that enable them is complex and indirect, requiring integrative frameworks to clarify the relationships between what transformations entail and the capacities needed to enable them. We develop the integrative transformative capacities framework (TCF) to conceptualize how sustainability transformations relate to the capacities to realize them in terms of the focal system and the strategies needed to bring about a desired change. We illustrate this framework, proposing key features of sustainability transformations, then identifying strategies for change associated with each feature and the capacities required to implement each strategy. We conclude by discussing some challenges of theorizing, identifying, and building transformative capacities and how the TCF addresses these challenges. This framework can help researchers be explicit about their assumptions and decisions about systems change, strategies to influence change, and the capacities to enable different actors to meaningfully contribute to 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 | 0.059 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.011 | 0.133 |
| Scholarly communication | 0.024 | 0.039 |
| Open science | 0.008 | 0.018 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 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".