Politics, housing and climate adaptation in Ottawa, Canada
Bibliographic record
Abstract
This article reflects on a turning point in how Canadians respond to climate change. The article summarizes research on urban flood risk and resilience in the city of Ottawa. The research involved semi-structured interviews with municipal representatives and developers in Ottawa, and began with extensive background exploration on the politics of urban development and climate change. Our findings indicate escalating debates between key public and private stakeholders—the regulators and producers of housing—regarding approaches to protecting neighbourhoods from flooding. Debates stem from inconsistent pressures imposed (or not imposed) by the market, insurers, three levels of government, geography, differing time horizons and ambiguities in climate projections. Overall, stakeholders appear siloed in their responses to climate change, which limits opportunities to collaborate on geographically-specific and community-based flood resilience. The project increased our understanding of how private and public sector actors negotiate policies, guidelines, and regulations intended to improve the resilience of Ottawa neighbourhoods. Our approach is unique, as there is scant research to date on how the building industry in Canada is responding to climate change and flood risk. The research adds to the growing body of Canadian scholarship on urban development and climate change adaptation. Research results are of interest to municipal policymakers, urban planners, urban studies researchers, the development industry, financial institutions, insurers, and urban sustainability advocates.
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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 | 0.004 |
| Science and technology studies | 0.028 | 0.005 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".