Beyond domain-led conceptualizations of urban zero-carbon transitions
Bibliographic record
Abstract
Rapid, systemic change is needed to achieve zero emissions, but there is uncertainty about how or where to intervene in urban systems. Drawing on the work of Donella Meadows, we apply a Leverage Points Perspective to identify and characterize points of system-level intervention that emerge from a study of climate action in Calgary, Canada, which was unique in applying a mixed set of academic approaches. Reflecting on Meadows’ and other frameworks for conceptualizing complex systems change, we discuss the challenge of conceptualizing change, a task of unique urgency in the context of the climate emergency. Too frequently, we argue, approaches focus attention on specific modes or forms of action seen to have the greatest opportunity for affecting change in place of the complex chains of actors, objects, and processes that collectively are the key to a deep and sustaining transition. We conclude by exploring how the insights of the Leverage Points Perspective and other approaches can be brought together to inform practical action, and by examining how related theoretical work on provisioning systems and applied work on urban Climate Commissions may be drawn on to advance understanding of how to deliver urban systems 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".