How do we study resilience? A systematic review
Bibliographic record
Abstract
Abstract The concept of resilience has gained immense popularity as a way to frame social and environmental challenges. However, its empirical operationalization and the integration of social and ecological dimensions continue to present difficulties. In this paper, we conduct a systematic review of existing empirical studies of resilience in social, ecological and social‐ecological systems (SESs) and examine how and to what extent these studies have achieved the operationalization of the concept of resilience. We evaluate the operationalization of resilience in 463 papers based on whether they define the system of interest and disturbances, whether they define resilience, whether they evaluate resilience, and for papers focusing on SESs, whether that evaluation integrates social and ecological dimensions. We find that 51% of empirical studies do not meet at least one of these operationalization criteria, and that even those that do often lack key features for effective operationalization, such as clear system boundaries and baseline state or an effective integration of social and ecological dimensions. Of the papers examining SESs and evaluating resilience, only 54% integrate social and ecological dimensions in that evaluation. Building on these findings, we propose some design guidelines for operationalizing future empirical studies of resilience. Read the free Plain Language Summary for this article on the Journal blog.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".