Growth over resilience: how Canadian municipalities frame the challenge of reducing carbon emissions
Bibliographic record
Abstract
In response to anthropogenic climate change, many governments are adopting policies to reduce carbon emissions. In Canada, federal and provincial governments have implemented carbon pricing. One of the effects of putting a price on carbon is increasing the cost of using private vehicles, which may reduce mobility and increase the risk of social exclusion, especially in contexts where car dependence is high. In this article, we analyse how municipal governments in Canada frame the challenges of climate change and reducing emissions, and examine whether they link these challenges to issues of mobility and social exclusion. Focusing on policies from four of Canada's largest cities – Calgary, Edmonton, Winnipeg and Vancouver – we identify four main frames used in planning documents: “the Growing City”, “If You Build It, They Will Come”, “Better City for All”, and “the Resilient City”. The Growing City frame is used to support status quo urban development, with climate mitigation options (including sustainable travel modes) optionally included for more concerned residents. This is the dominant frame in Calgary, Edmonton, and Winnipeg. Conversely, Vancouver uses the Resilient City frame to indicate that climate mitigation and adaption strategies are essential, and all citizens must be prepared for change. Vancouver presents changes to mobility as necessary for all, rather than an option for some. Social exclusion is not explicitly addressed in the frames, though it is presented as a reason to support building alternative transportation or more public spaces. Social exclusion receives little consideration as a potential consequence of climate mitigation policies.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.052 | 0.013 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".