Guiding Systemic Change: A cross-case analysis of ‘transition labs’ in Canada and Sweden
Bibliographic record
Abstract
Agenda 2030 presents a global ambition to transform our world into sustainability. In this study we seek to advance knowledge on how sustainable systemic change can be guided in practice, and how the keywords of Agenda 2030 can provide value in such work. We argue that the conception of sustainability and desire for positive change may form strong coalitions and motivators for realizing transitions challenging the status quo. This study seeks to make a practical contribution into some of the methodologies, processes, tools and techniques that may be useful in guiding systemic change: with an emphasis on backcasting and a multi-level model for transitions. The study is exploratory in its approach, building on a description, comparison and cross-case analysis of two lab methodologies and insights from their application in concrete cases: the Energy Futures Lab in Alberta, Canada and the Challenge Lab in West Sweden. The analysis is guided by a novel analytical framework operationalizing keywords of Agenda 2030 to shed light on how sustainability transition processes (including transition labs) may contribute to sustainability transitions. The framework itself, and the explorative comparison and analysis pose some questions that may inspire further development of transition lab methodologies to have a transformative impact across systems.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".