Reconsidering disaster resilience: a nonlinear systems paradigm in agricultural communities in Southern Africa
Bibliographic record
Abstract
Disasters continue to have a dramatic impact on lives, livelihoods and environments communities depend on. In response to these losses, the global community has developed various theories, assessment methodologies and policies aimed at reducing global losses. A contemporary outcome of these interventions is to build the disaster resilience. However, despite the disaster resilience-building endeavours espoused by policies, theories and methodologies, very little progress is being made in reducing disaster losses. This paper argues that a possible reason behind the limitations of current resilience-building policies and methodologies could be that most of these policies are based a mechanistic scientific paradigm that places an emphasis on system components that are perceived to build resilience and not the function of systems as a whole. This often leads to resilience-building initiatives that are based on a ‘one-size-fits-all’ approach. This paper argues for the use of a complex adaptive systems approach to building resilience. This approach argues that contextual factors within different social systems will have a nonlinear affect on disaster resilience-building efforts. Therefore, it is crucial to move away from ‘one-size-fits-all’ approaches to more flexible approaches to building resilience. These hypotheses are tested by means of a correlation statistical analysis of agricultural communities in Southern Africa. Results of this analysis indicate that unique resilience profiles are evident in almost all of the communities studied. This indicates that resilience is not the same for everybody, and that resilience-building endeavours should be flexible enough to be adapted for different contexts.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".