Growth over resilience: how Canadian municipalities frame the challenge of reducing carbon emissions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it