Municipal Climate Change Governance: A Pathway to Resilience-Building and Vulnerability-Reduction A Case Study Of Kingston, Ontario
Bibliographic record
Abstract
In 2019, Kingston became the first municipality in Ontario to declare a ‘climate emergency’. This declaration stimulated further commitments to reduce greenhouse gas emissions and to enhance community resilience to adapt to climate change impacts. The present research aims to evaluate Kingston municipal government’s climate policies to understand strengths, weaknesses, and areas for potential improvement. Formulated upon the analytical frameworks of adaptive governance and political ecology, this qualitative research triangulated content analysis of the documents produced by the City of Kingston with semi-structured interviews with the City officials and social justice advocates whose works are related to climate policies and the most vulnerable population in the face of climate change. The results of this study are summarized through three main arguments. First, adaptive governance and resilience thinking elements are present in Kingston’s climate policies. However, they need to be further and more explicitly developed to shape the policies in future. Second, Kingston emphasizes scientific framing and technical solutions for reducing emissions over the contextual and human security framing and adaptation. Consequently, the idea of climate vulnerability and importance of justice-oriented approach to avoid maladaptation and unintended effects of adaptation on marginal groups is not integrated in its climate change plan so far. Finally, to address this gap, Kingston needs to create a new policy document with a stronger equity and justice orientation within both adaptation and mitigation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".