Integrating diversity and agency into social-ecological resilience metrics
Bibliographic record
Abstract
Resilience is an increasingly popular concept in research and practice, but quantitative resilience analyses are often disconnected from resilience theory. For example, previous studies argue that diversity, a key attribute for building resilience, and agency, essential for understanding local adaptation and transformation, are critical to understanding resilience. Despite significant progress in integrating them into qualitative frameworks, diversity and agency are rarely incorporated into quantitative social-ecological resilience metrics. This omission is concerning, given the critical role of quantitative resilience metrics in informing resilience-oriented decision-making. This study examines how diversity and agency are represented in quantitative resilience metrics across disciplines, with the goals of (a) assessing how research on social-ecological resilience currently integrates these concepts into quantitative metrics and (b) identifying future opportunities to enhance their inclusion using insights from other fields. Using topic modelling to identify different research fields and facilitate the screening process, we performed a multidisciplinary systematic meta-review of resilience metrics. To understand what types of resilience metrics are used across disciplines and where diversity and agency are more commonly included, we identified six categories of resilience metrics, with “performance under disruption” being the most used category (35%). We found that a limited number of quantitative resilience metrics include diversity and agency, with “system structure” and “compound indicators” being the main sources of diversity and agency, respectively. We further reviewed simulation models applying resilience metrics. The prevalence of performance under disruption metrics is stronger than in reviews (67%) and a similar quantity of metrics including diversity (14%) and agency (5%) was found. Drawing on insights from multiple disciplines, we outline five potential pathways to improve the inclusion of diversity and agency in social-ecological resilience metrics: using network-based metrics, using response and pathway diversity, including diversity and agency in compound indicators, integrating quantitative methodologies outside resilience theory, and improving the application of resilience in simulation models.
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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 | 0.060 | 0.189 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.033 | 0.027 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".