A Methodological Framework to Initiate and Design Transition Governance Processes
Bibliographic record
Abstract
Abstract: Sustainability transitions require societal change at multiple levels ranging from individual behavioral change to community projects, businesses that offer sustainable products as well as policy-makers that set suitable incentive structures. Concepts, methods and tools are currently lacking that help to initiate and design transition governance processes based upon an encompassing understanding of such diverse interactions of actors and intervention points. This article presents a methodological framework for the initiation and design of transition governance processes. Based upon a conceptualization of sustainability transitions as multilevel learning processes, the methodological framework includes participatory modeling, a systematic literature review and governance system analysis to identify social units (learning subjects and contexts), challenges (learning objects) and intervention points (learning factors) relevant for initiating case-specific transition governance processes. A case study on sustainable food systems in Ontario, Canada is provided to exemplify the application of the methodological framework. The results demonstrate the merit of combining stakeholder-based and expert-based methods, as several learning factors identified in the participatory process could not be found in the general literature, and vice versa. The methodological framework allowed for an integrated analysis of the diversity of existing initiatives in the case study region and specific intervention points to support place-based sustainability innovations. Initiators of transition governance processes can use the results by designing targeted interventions to facilitate and coordinate existing initiatives or by setting new impulses through purposeful action.
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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 | 0.132 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".