Advancing neighbourhood climate action: opportunities, challenges and way ahead
Bibliographic record
Abstract
Abstract Cities are emerging as key sites for action on climate change. Within cities, urban neighbourhoods are increasingly taking leadership in addressing local effects of climate change through mitigation and adaptation programs. Bottom-up action on climate change through neighbourhood scale programs presents opportunities in terms of getting the community to partner and participate in climate action. However, neighbourhood scale programs often run into challenges in terms of limited participation, impact and resources to keep the programs running. In this paper, we advance the literature on the opportunities and challenges of neighbourhood scale climate action. We do so by analysing three neighbourhood scale programs that address climate action in Canada and in Australia. We adopt online workshops as a research methodology where volunteers from the three programs share their experiences of opportunities and ways of overcoming challenges of neighbourhood climate action. Our findings illustrate that collaborative governance between the city and the neighbourhoods, incremental community building and consolidating local resources are important for advancing neighbourhood climate action. This paper adds to the thin body of knowledge on neighbourhood scale climate action and presents ways of overcoming the challenges of bottom-up climate action.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".