From Resistance to Transformation: A Generic Metric of Resilience Through Viability
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".