From risk management to quantitative disaster resilience – a paradigm shift
Bibliographic record
Abstract
There are practical links between disaster risk management and sustainable development leading to the reduction of disaster risk and re-enforcing resilience as a new development paradigm.There has been a noticeable change in disaster management approaches, moving from disaster vulnerability to disaster resilience; the latter viewed as a more proactive and positive approach.As hazard is increasing, at the same time, it erodes resilience.In the past, standard disaster management considered arrangements for prevention, mitigation, preparedness and recovery, as well as response.However, over the last 10 years substantial progress has been made in establishing the role of resilience in sustainable development.Multiple case studies around the world reveal links between attributes of resilience and the capacity of complex systems to absorb disturbance while still being able to maintain a certain level of functioning.There is a need to focus more on action-based resilience planning.Disasters do not impact everyone in the same way.It is clear that the problems associated with sustainable human wellbeing call for a paradigm shift.Use of resilience as an appropriate matrix for investigation arises from the integral consideration of overlap between: (a) physical environment (built and natural); (b) social dynamics; (c) metabolic flows; and (d) governance networks.This paper provides an original systems framework for quantification of resilience.The framework is based on the definition of resilience as the ability of systems to absorb disturbance while still being able to continue functioning.The disturbance depends on spatial and temporal perspectives and direct interaction between impacts of disturbance and system adaptive capacity to absorb disturbance.
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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 | 0.024 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.035 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".