A pioneering approach to measure increased resilience to face climate change: insights from the Race to Resilience campaign
Bibliographic record
Abstract
Abstract This paper illustrates a methodology to measure the impact of resilience-building actions on the increased resilience of people and natural systems to face climate change, developed and field-tested around the Race to Resilience Campaign. Despite increasing acknowledgment of the need for robust methodologies and indicators to monitor and evaluate efforts across adaptation planning and implementation, and provide credibility, accountability and transparency to such actions, there is still a lack of sufficiently standardized and agreed upon metrics able to capture the effect of resilience-building actions. The proposal illustrated in this manuscript offers a pioneering approach for high-level tracking, monitoring and evaluation of resilience-building efforts of non-state actors, based on two complementing sets of metrics: depth metrics measure the degree to which an action is generating a change to fundamental conditions which can demonstrably be related to increasing resilience; while magnitude metrics offer a quantification of the beneficiaries that are affected by these changes. Underlying both stand the Resilience Attributes: properties which can be soundly associated with triggering resilience across different systems, and which can then be used to assess increased resilience ‘by proxy’: that is, by seeing how an action sets forth changes in properties commonly associated with resilience. These Attributes were identified based on updated scientific literature and co-construction exercises with global experts. The integration of Depth and Magnitude indices, adjusted by a Confidence Index evaluating data reliability, allows to estimate the overall contribution of a set of actions on increasing resilience against climate challenges. Based on the above, a possible Monitoring & Evaluation cycle is proposed, and an illustration is offered on two case studies from the Race to Resilience campaign. Key strengths, lessons learned and insights are summarized to stimulate the global discussion, in the context of the Global Stocktake and Global Goal on Adaptation.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".