Assessing Canadian Municipal Climate Change Adaptation Plans: Investigating Equity Considerations in Adaptation Planning
Bibliographic record
Abstract
This research examines whether Canadian municipalities are integrating equity considerations into their local adaptation plans. I also examine whether population size, region, and consulting group involvement influence adaptation plan quality and equity considerations. My research questions are as follows: \n1. Do Canadian municipalities consider equity in relation to adaptation planning? \n2. Are vulnerable and marginalized groups included in the adaptation planning process? \n3. Are local adaptation plans likely to reduce vulnerability for marginalized groups? \nI performed a content analysis on 67 official municipal adaptation planning documents, qualitatively coding them for plan quality indicators and equity considerations. The findings reveal that neither population size nor regional affiliation significantly influences the adoption of an equity lens in adaptation plans. Moreover, there is limited insight into vulnerability reduction for marginalized communities, and the participation of these communities in the planning process is weak across all municipalities. Equity is most commonly discussed in relation to the fact base of local plans. The lack of implementation details in many plans and a deficiency in monitoring and evaluation data hinders the ability to assess whether the plans are effective in reducing vulnerability for these groups. This study emphasizes the need to move beyond symbolic gestures, urging governments to actively prioritize equity considerations in adaptation planning for the resilience and wellbeing of all community members to address the complex challenges of climate 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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".