Rethinking Resilience: Reflections on the Earthquakes in Christchurch, New Zealand, 2010 and 2011
Bibliographic record
Abstract
Resilience has emerged as a policy response in an era of public concern about disasters and risks that include fear of terrorism and environmental or economic catastrophe. Resilience is both a refreshing and a problematic concept. It is refreshing in that it creates new opportunities for interdisciplinary research and vividly reminds us that the material world matters in our social lives, political economy, and urban planning. However, the concept of resilience is also problematic. Widespread, uncritical calls for greater resilience in response to environmental, economic, and social challenges often obscure significant questions of political power. In particular, we may ask, resilience of what, and for whom? My reflection here was written in the context of the ongoing grief, disruption, and community protest in my home city of Christchurch, New Zealand, a city that experienced 59 earthquakes of magnitude 5 or more, and over 3800 aftershocks of magnitude 3 or greater between September 2010 and September 2012. From this perspective, I call for expanding our political imagination about resilience, to include ideas of compassion and political resistance. In my observation, both compassion, expressed as shared vulnerability, and resistance, experienced as community mobilization against perceived injustice, have been vital elements of grassroots community recovery.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.025 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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".