From Resistance to Transformation: A Generic Metric of Resilience Through Viability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the last two decades resilience has emerged as a promising concept that can help societies and more generally social‐ecological systems become less vulnerable to shocks and stressors. As such it has been adopted by a large number of disciplines—from psychology, physics, and ecology to disaster risk reduction, climate change adaption, and humanitarian and food security interventions. However, although numerous definitions or measures of resilience have been proposed, those were mainly discipline centered and, as such, failed to provide an adequate overarching framework. This paper explores the question of the formalization and measurement of resilience, with the objective to develop a generic metric that applies across the disciplines and to the different interpretations of resilience. Building on the definitions found in the literature, a continuum of five categories of resilience responses is identified: (i) resistance, (ii) coping strategies, (iii) adaptation, (iv) adaptive preference, and (v) transformation. Those categories are then reframed into a generic metric, using viability analysis—a mathematical formalism which builds on dynamic systems and control theory. Theoretical and empirical analyses are then conducted, looking in particular at how inertia and costs associated with the types of responses influence the level of resilience. To illustrate this new metric, we draw on two models widely discussed in the resilience literature: the exploitation of renewable resources and the case of lake eutrophication. Both theoretical and numerical analyses demonstrate the relevance of the typology as a generic framework for resilience but also highlight transformation as a particular case of resilience response.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it