Closing the equity deficit: Sustainability justice in municipal climate action planning in Waterloo region
Bibliographic record
Abstract
There is growing recognition that often well-intended climate action solutions perpetuate and exacerbate manifestations of colonialism and racism due to the lack of equity and justice considerations in designing and implementing these solutions. There is limited research exploring why the integration of these considerations are lacking in municipal climate action planning. This exploratory descriptive qualitative study explored how municipal actors perceive and understand equity and justice in municipal climate action planning as a step toward addressing this issue. Semistructured interviews were conducted with seven members of the core management group from ClimateAction Waterloo region, and a template analysis of the interview data resulted in six themes. Findings suggested that those involved in municipal climate action planning understand and perceive justice and equity considerations as important to their work, however, translating this understanding to practice is a challenge due to structural (governmental and societal) and capacity (limited time, funding, resources, and knowledge) barriers. By better understanding how key actors consider justice and equity, we identify shifting colonial mental models as a potential pathway for transformative change given the central role of these actors.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".