Measuring and assessing resilience: broadening understanding through multiple disciplinary perspectives
Bibliographic record
Abstract
Summary Increased interest in managing resilience has led to efforts to develop standardized tools for assessments and quantitative measures. Resilience, however, as a property of complex adaptive systems, does not lend itself easily to measurement. Whereas assessment approaches tend to focus on deepening understanding of system dynamics, resilience measurement aims to capture and quantify resilience in a rigorous and repeatable way. We discuss the strengths, limitations and trade‐offs involved in both assessing and measuring resilience, as well as the relationship between the two. We use a range of disciplinary perspectives to draw lessons on distilling complex concepts into useful metrics. Measuring and monitoring a narrow set of indicators or reducing resilience to a single unit of measurement may block the deeper understanding of system dynamics needed to apply resilience thinking and inform management actions. Synthesis and applications . Resilience assessment and measurement can be complementary. In both cases it is important that: (i) the approach aligns with how resilience is being defined, (ii) the application suits the specific context and (iii) understanding of system dynamics is increased. Ongoing efforts to measure resilience would benefit from the integration of key principles that have been identified for building resilience.
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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 | 0.126 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.015 | 0.007 |
| Science and technology studies | 0.004 | 0.041 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.006 | 0.014 |
| 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".