Explorative transition governance: Understanding by engaging in transitions in the making
Bibliographic record
Abstract
Transition governance, a field within sustainability transitions research, explores how societal transitions can be accelerated towards just and sustainable futures. This chapter presents the explorative and engaged approach to transition governance, which supports societal actors in navigating the complex and uncertain dynamics of ‘transitions in the making.’ A key feature of explorative transition governance is the interplay between analytical and action-oriented, transdisciplinary approaches. The chapter begins by outlining the background and diversity of analytical approaches to understanding how actors interact within transition dynamics, ranging from strategies of resistance to strategies for transformation. It introduces the X-curve framework, an analytical tool that enables identification of the actors and roles required in the twin dynamics of build-up and phase-out. Followed by introduction of the transformative and transdisciplinary approaches that have emerged under the umbrella term Transition Management: a cyclical approach based on strategic, tactical, operational and reflexive activities for social learning in applied multi-actor settings. These include transition arenas, reflexive monitoring, back-casting and transition experiments. Finally, the chapter reflects on the challenges and opportunities for advancing explorative transition governance amidst growing resistance to and need for transformative 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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".