Inequalities in the adaptive cycle: reorganizing after disasters in an unequal world
Bibliographic record
Abstract
Natural hazards can trigger disasters that lead to the collapse and reorganization of social-ecological systems. This reorganization can involve systems transitioning to more positive trajectories. The Panarchy framework, which conceptualizes social-ecological systems as dynamic interrelated adaptive cycles, is a common conceptual framework for understanding system reorganization. However, it is unclear how inequalities, social mechanisms known to influence disaster recovery outcomes, shape a system's adaptive cycle post-disaster. Understanding the roles of inequalities can help develop social-ecological models to identify processes that build resilience into disaster recovery. We applied the Panarchy framework to inform propositions describing how inequalities can influence the reorganization of social-ecological systems after disasters triggered by natural hazards. We qualitatively analyzed a selection of case studies that discussed inequalities pre- and post-disasters and related these to adaptive-cycle system characteristics (i.e., potential, connectedness, and resilience). We identified three propositions: 1) The ability of groups to reorganize after a disaster varies across the inequality spectrum; 2) The reorganizing abilities of groups across the inequality spectrum impact one another; and 3) The presence of inequalities affect connectedness within the system. Incorporating these propositions into social-ecological system modeling can improve our understanding of how inequalities impact system reorganization. This information can support disaster recovery plans that strengthen a system's ability to enter a more positive trajectory post-disaster.
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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 | 0.000 |
| 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".