Resilience implications of policy responses to climate change
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This article examines whether some response strategies to climate variability and change have the potential to undermine long‐term resilience of social–ecological systems. We define the parameters of a resilience approach, suggesting that resilience is characterized by the ability to absorb perturbations without changing overall system function, the ability to adapt within the resources of the system itself, and the ability to learn, innovate, and change. We evaluate nine current regional climate change policy responses and examine governance, sensitivity to feedbacks, and problem framing to evaluate impacts on characteristics of a resilient system. We find that some responses, such as the increase in harvest rates to deal with pine beetle infestations in Canada and expansion of biofuels globally, have the potential to undermine long‐term resilience of resource systems. Other responses, such as decentralized water planning in Brazil and tropical storm disaster management in Caribbean islands, have the potential to increase long‐term resilience. We argue that there are multiple sources of resilience in most systems and hence policy should identify such sources and strengthen capacities to adapt and learn. WIREs Clim Change 2011 2 757–766 DOI: 10.1002/wcc.133 This article is categorized under: Vulnerability and Adaptation to Climate Change > Learning from Cases and Analogies
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 it