Urban Climate Resilience Discourse in Ontario, Canada: Findings from a Systematic Literature Review and Thematic Analysis
Bibliographic record
Abstract
Background: This review set out to investigate how academic literature addresses urban climate resilience in the context of Ontario, Canada. Considering the province’s large population, economic importance, and increasing climate-related challenges, the review also aimed to uncover gaps in the research and areas needing further exploration. Methods: Literature was screened based on pre-determined criteria for relevance to urban climate resilience in Ontario. Text snippets were extracted and analyzed using inductive coding and thematic analysis, and descriptive statistics summarized key features of the articles. Article quality was assessed using JBI critical appraisal tools. Results: A total of 192 articles were identified, with 22 meeting the inclusion criteria. Most articles were published during or after 2020, with subjects including green infrastructure, governance, and public health. The codes generated from inductive analysis were organized into four key themes—adaptive governance, knowledge exchange, community engagement, and equity and health. Thematic findings highlight the need for flexible climate governance, cross-sector collaboration, deeper community engagement, and greater equity in public health and resilience planning, emphasizing inclusive, localized approaches to urban climate resilience in Ontario. Conclusion: The review highlights that urban climate resilience in Ontario is closely linked to climate action, with a focus on adaptation at the local level. Key gaps in Ontario’s ability to build urban climate resilience include the need for more effective knowledge exchange and the adoption of adaptive governance strategies. Collaborating with the private sector and exploring public–private partnerships could enhance resilience-building efforts.
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 | 0.025 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.020 | 0.044 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".