Measuring Progress on Climate Change Adaptation: Lessons from the Community Well-Being Analogue
Bibliographic record
Abstract
While research on assessing climate change adaptation (CCA) activities is in the nascent stage of development, measuring similar endeavours within the community well-being (CWB) field is well established across Canada and internationally through the use of indicators and associated measures. CCA activities are an important part of building resilience to climate-induced natural disasters, and reducing secondary hazards arising from damage to critical infrastructure and other essential facilities. This study evaluated the CWB analogue to provide lessons for the measurement of progress and adaptation to climate change at the municipal level. Since the impacts of climate change are experienced at the local scale and effective CCA is thought to require local scale engagement and targeted action, municipal scale measurement is key to understanding CCA progress. Research involved an extensive review of CWB models and key informant interviews conducted with key Canadian municipal and international authorities who are leaders in spearheading CWB initiatives. In the paper we outline the major CWB models, our findings from the CWB analogue and the lessons learned for CCA measurement. In particular, we suggest that early engagement and participative processes, flexible and adaptable measurement tools, careful consideration of data requirements, mainstreaming CCA measurement into ongoing activities and the de-siloing of expertise will be important for the success of CCA measurement activities at the municipal scale.
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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 | 0.027 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".