You have declared a climate emergency…now what? Exploring climate action, energy planning and participatory place branding in Canada
Bibliographic record
Abstract
The negative impacts of climate change are becoming increasingly clear and cities around the world are a driving force behind these problems, accounting for over 70% of all greenhouse gas emissions. In recognition of the need to act quickly, over 2300 jurisdictions, including 653 in Canada, have recently made climate emergency declarations (CEDs). Yet because most of these CEDs have only been made over the past few years, very little research has been completed focused on what cities are doing after making these decisions. Informed by a literature review on CEDs, urban governance, citizen engagement, communication and place branding strategies, we seek to advance understanding in this important area. To do so, we present a study that centered around two Decision Theatre workshops conducted with climate, energy and communication professionals (n = 12) working for or with local governments in four Canadian cities that have declared CEDs. Workshops were transcribed and analyzed via thematic analysis to identify and understand a series of solutions and challenges facing cities. The top solutions recorded were creating targets/action plans, the importance of collaboration, and sharing information with communities. The top two challenges identified were the diversity of city staff and getting the message out. The study closes with a discussion of the broader implications of this work, including recommendations for cities and calls for future research in this critical area.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.034 | 0.014 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".