Integrative resilience in action: Stories from the frontlines of climate change and the Covid-19 pandemic
Bibliographic record
Abstract
Interest in resilience and vulnerability has grown remarkably over the last decade, yet discussions about the two continue to be fragmented and increasingly ill-equipped to respond to the complex challenges that systemic crises such as climate change and the Covid-19 pandemic pose to people, places, and the planet. Institutional interventions continue to lag behind, remaining predominantly focused on technocratic framings of vulnerability and resilience that do not lead to a more robust engagement with the reality of the changes that are underway. This paper provides a blueprint for facilitating intersectional resilience outcomes that ensure that as a society we are not merely surviving a crisis, but are committing to interventions that place equity, solidarity, and care at the center of healthy adaptation and wellbeing. First, it traces the evolution of resilience from a strictly ecological concept to its uptake as a socio-ecological framework for urban resilience planning. Next, it argues that current framings of vulnerability should be expanded to inform interventions that are locally relevant, responsive, and “bioecological.” The integrative resilience model is then introduced in the second half of the paper to challenge the scope of formal resilience plans while providing an entry point for renewed forms of resistance and recovery in the age of neoliberalism-fueled systemic crisis. The three pillars of the model are discussed alongside a selection of scalable and adaptable community-driven projects that bring this approach to life on the ground. By being rooted in lived experience, these innovative initiatives amplify and advance the work of frontline communities who are challenging and resisting the neoliberalization not only of urban governance and resilience, but of wellbeing and (self-) care more broadly.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.053 |
| Scholarly communication | 0.019 | 0.024 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.012 | 0.025 |
| Insufficient payload (model declined to judge) | 0.004 | 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".