Systemic resilience: principles and processes for a science of change in contexts of adversity
Bibliographic record
Abstract
Despite the increasing popularity of discussions of resilience in disciplines as diverse as ecology, psychology, economics, architecture, and genetics (among many others), researchers still lack a conceptual model to explain how the resilience of one system relates to the resilience of other cooccurring systems.Models that explain resilience within a single system are more robust and better studied.Although some researchers argue that both ontological and epistemological weaknesses prevent such an integrated model from being developed (the incommensurability hypothesis), others have carried out metasyntheses using techniques like network citation analysis to identify common principles and processes that are associated with resilience across disciplines.Although useful, metasyntheses have yet to identify sufficient commonalities across bodies of research to account for a single model of resilience.This paper adapts methods used for the thematic synthesis of qualitative data to critically analyze metasyntheses of resilience and identify principles that explain patterns of resilience of different systems (biological, psychological, social, cultural, economic, legal, communication, and ecological systems are all considered).Sixteen purposefully selected published syntheses were reviewed, along with dozens of other supporting peer-reviewed articles and book chapters, supplemented by consultations with knowledge experts.Seven common principles across systems were identified.These include: (1) resilience occurs in contexts of adversity; (2) resilience is a process; (3) there are trade-offs between systems when a system experiences resilience; (4) a resilient system is open, dynamic, and complex; (5) a resilient system promotes connectivity; (6) a resilient system demonstrates experimentation and learning; and (7) a resilient system includes diversity, redundancy, and participation.Where evidence refutes a principle, discordant findings are highlighted.Together, these principles account for resilience as a sequence of systemic interdependent interactions through which actors (whether persons, organisms, or ecosystems) secure the resources required for sustainability in stressed environments.
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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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.005 | 0.051 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".