Disastrous consequences: shortcomings of resiliency strategies for coping with accelerating environmental change
Bibliographic record
Abstract
Natural disasters driven by climate change have increased in frequency, intensity, and scale. The consequences of these disasters include the loss of human lives, property damage, increased economic costs, and decreased ability to respond effectively to both abrupt and more gradual disasters. Government responses to such disasters are often based on a desire to rapidly recover to normal, which is understandable, but is difficult in the Anthropocene because of rapidly changing social-ecological baselines that exceed the limits of adaptation and mitigation. Here we identify pitfalls of a narrow and singular focus on resiliency. Resiliency focuses on efficient and rapid recovery, which is laudable, but assumes linear responses, absence of tipping points, a single scale of cause and effect, and an implicit assumption of stationarity. In contrast, we highlight the importance of social-ecological resilience, which includes resiliency but also accounts for multiple spatial and temporal scales, cross-scale effects, and most importantly, the possibility of alternative system configurations (or regimes) separated by tipping points. Social-ecological resilience provides a more comprehensive and realistic framing, and therefore the ability to persist with change, prepare for, and perform adaptation and transformation of social-ecological systems. Accounting for social-ecological resilience is essential for governance of coupled systems of humans and nature as we collectively face a future in the Anthropocene that will contain more surprising and unpredictable events propelled by global change including climate change.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".