A scoping review of how the seven principles for building social-ecological resilience have been operationalized
Bibliographic record
Abstract
Just over ten years ago, resilience scholars proposed seven principles for enhancing the resilience of social-ecological systems. The authors argued that there was a pressing need for a better understanding of how the principles can be operationalized. Through a scoping review we evaluate how these principles have been operationalized, which we define as a process of moving a concept from the theoretical to the measurable using, in this case, resilience principles divided into component dimensions and identifying measurable indicator(s) for each dimension. Here we show that the seven resilience principles have been vastly underutilized as a tool for operationalizing social-ecological resilience. Of more than 750 articles citing the principles, just 23 operationalized them and only seven of these articles operationalized all seven principles. Several of those 23 articles were unclear in the ways in which operationalization occurred. In terms of geography, the focus of the majority of articles was in the Global North. Articles that operationalized the principles used a wide variety of dimensions and indicators. To advance the scholarship and practice of building social-ecological resilience, we recommend the use of a consistent set of dimensions, or “parts that make up the whole” for each resilience principle combined with contextualized indicators or measures. Following these recommendations will create the capacity for global analyses and insights while honoring the local context that creates unique conditions in each place. Further, using contextualized indicators allows for plural approaches to operationalizing social-ecological 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 | 0.047 | 0.177 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.032 | 0.035 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".