Shifting and sharing power in urban climate justice work: experiments in transformative learning in Vancouver, Canada
Bibliographic record
Abstract
As the global reckoning with a changing climate increases in urgency, and the real-world consequences of delayed and inadequate action become impossible to ignore, city leadership continues to grow in response. Cities are making significant shifts in policy and regulation, investing in infrastructure, building strong cross-sectoral collaborations, experimenting with solutions, advocating for changes outside their jurisdiction, and taking other important actions. Alongside these activities is a growing critique that climate action is not adequately integrating principles and goals of justice, equity, inclusion, or decoloniality. In this article we argue that transformative learning is an underutilized theory and practice when working toward city-based just climate action. We describe transformative learning approaches and implications in running a Climate Justice Field School in Vancouver, Canada, a response to implementing the first ever Climate Justice Charter for the city. This work resulted in five transformative learning interventions for urban climate researchers and practitioners to engage with as they move toward just, equitable, inclusive, decolonial climate action.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.009 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".