“The Big Ship Turns Around Slowly”: An Evaluation of Equity and Justice in Ontario Climate Action Plans
Bibliographic record
Abstract
Here I report the results of an evaluation of climate action plans for three cities in Ontario (Canada). The evaluation was guided by a framework containing criteria for assessing the degree to which climate action plans address distributive, procedural, recognition, and healing justice. The framework was developed through consultations with climate change scholars and representatives from equity-deserving groups. Results suggest there is considerable risk that equity-deserving groups will be left behind in the transition to more climate-friendly communities. Findings are based on empirical evidence from the plan evaluation and qualitative evidence from interviews with representatives from equity-deserving groups in the case study cities. I offer recommendations for how planners and other urban actors can integrate equity considerations into municipal climate action planning. Planners can (a) actively support equity-deserving communities in transcending historical discrimination by specifying how underserved groups are part of the future vision for a community, (b) frame justice as a core focus of the planning process rather than an end that planning practice should strive to achieve, (c) address the current gap between best practice and actual practice regarding meaningful engagement of underserved communities, and (d) more carefully consider the potentially regressive outcomes of climate action. These actions require planners to educate themselves about the underlying drivers of inequity in a specific community.
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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 | 0.072 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".