Disastrous resonances: Social memory dynamics and extreme events. Key questions for cultural loss and landscape sustainability
Bibliographic record
Abstract
Sustainability research has mostly ignored the resonant effects of the loss of key cultural information on disaster reduction, and disasters eroding such knowledge. This gap in the literature is especially salient in a rapidly changing world, where we often find that he relevant memory on how to cope with Extreme Events (EE) is not accessible to decisionmakers. In this perspective piece we focus on the combined effect of loss of skill, and experience and the strikes of disasters can act in a complex, resonant way to precipitate unforeseen effects that cascade across interconnected systems. This destructive resonance can potentially generate sudden collapses, difficult or impossible to recover from. This non-linear (unpredictable) susceptibility and lack of relevant experience is especially meaningful in landscapes where both environments and management practices are changing rapidly. In these cases, the access to accumulated memory and skill is easily disconnected. In our present world big --but also relatively small-- perturbations can trigger a complex interconnected cascading failure at even bigger scales. To address this complex and unexplored scenario, we propose a framework focusing on the interconnection between two social memory scales and two EE scales through four management stages: Mitigation, Preparation, Action, Recovery (MPAR). At the social level, community and administrative structures differently maintain, acquire and lose expertise on MPAR practices. At the EE level fast and slow-paced impacts (e.g. floods, tsunamis vs droughts, harvest failures), need different MPAR strategies. This basic framework allows to simplify the resonant scenario, allowing to initially model independently how loss of key MPAR practices increases susceptibility to disasters, and how disasters trigger the cultural loss of key MPAR practices. To this end we propose two strategies: 1) adapt coevolutionary biological and flooding models for coupling cultural loss affecting MPAR to disasters, 2) adapt network cascading science to model how impacts on social networks percolate to MPAR memory networks.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".