“Is Resilience Useful, Usable, and Used? Outlining the Social Characteristics of a Resilient System”
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic has underlined the international priority to systemically operationalise resilience in the face of increasing prevalence of complex and cascading hazards. This concept paper identifies the components of a resilient society, establishing the usefulness and usability of the application of ‘resilience’, and proposes the characteristics used by a resilient system. Method: Through the review of case-based examples and previously published concept papers, this paper underwent a concept analysis to understand and qualify the characteristics of a resilient community. Through extensive research and critical analysis of disaster risk responses both effective and not, the authors condensed the literature to identify the key components of a resilient society. Results: To respond to this evolving landscape of disaster risk, community and governmental responses should be collaborative in order to be successful and sustainable to increase resilience across communities, societies and networks. To unpick the complexity of how communities and governments might promote resilience effectively, we explore whether community and social capital are useful resources to create and sustain resilient approaches to disaster risk reduction and management. We consider that by exploring how social capital links, bridges and bonds actors within a system are qualitative key facets of a resilient community. A resilient system is the product of trust and collaboration between asset-based networks of bonded and bridged communities and risk and support-based networks of bonded and bridged organizations. Conclusion: By evaluating the usefulness and usability of the concept, we consider that a resilient system is an iterative learning process, asset based, trusting across power and resource gradients and is best built before or even if essential during a crisis. Noting that resilience is a dynamic process which requires integrated collaboration and continual adjustment to develop a sustainable framework, we consider that social characteristics of a resilient system are useful, useable and should be used.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".