A climate resilience research renewal agenda: learning lessons from the COVID-19 pandemic for urban climate resilience
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
Learning lessons from the COVID-19 pandemic opens an opportunity for enhanced research and action on inclusive urban resilience to climate change. Lessons and their implications are used to describe a climate resilience research renewal agenda. Three key lessons are identified. The first lesson is generic, that climate change risk coexists and interacts with other risks through overlapping social processes, conditions and decision-making contexts. Two further lessons are urban specific: that networks of connectivity bring risk as well as resilience and that overcrowding is a key indicator of the multiple determinants of vulnerability to both COVID-19 and climate change impacts. From these lessons three research priorities arise: dynamic and compounding vulnerability, systemic risk and risk root cause analysis. These connected agendas identify affordable and healthy housing, social cohesion, minority and local leadership and multiscale governance as entry points for targeted research that can break cycles of multiple risk creation and so build back better for climate change as well as COVID-19 in recovery and renewal.
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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it