Climate Change, Urban Responses and Sociospatial Transformations: The Example of Quebec City
Bibliographic record
Abstract
The growing involvement of cities in the fight against climate change is probably one of the most significant features of today’s environmental governance. Beyond contributing to mitigation and adaptation efforts, urban climate action also helps in understanding how urban societies and spaces are being transformed in a context of global environmental change. This paper looks in particular at these sociospatial transformations, by presenting an empirical research on Quebec City’s climate policy. Since 2004, Quebec City has implemented various mitigation initiatives, but without being able to reduce its emissions. In fact, its policy approach has been mainly symbolic and has not encouraged the institutionalization of the climate issue in planning and governance practices. The case of Québec City shows that climate change is contributing to the renewal of environmental policies, but it also highlights the difficulty of decarbonizing urban socio-technical systems that have mainly developed around automobility.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".