Community resilience implications for institutional response under uncertainty: Cases of the floods in Wayanad, India and the earthquake in <scp>Port‐au‐Prince</scp>, Haiti
Bibliographic record
Abstract
Abstract While trajectories of community resourcefulness, solidarity, and mutual trust during and following environmental crises are abound in the literature, how these trajectories are taken into consideration and influences spatial planners, humanitarians and decision makers working under uncertainty remains under documented. Our article explores the concept of community resilience in action to illustrate where community resilience in action is supported, hindered or ignored by the state and non‐state organizations. Through an inductive epistemological approach, it draws examples from the exploratory fieldwork of two case studies and interviews in settlements in developmental contexts where the inhabitants, built environments and livelihoods have been severely affected following a hazardous event. Using observations and testimonies from Wayanad, a hill district in India affected by heavy monsoon floods in 2018 and 2019, and from marketplaces in Port‐au‐Prince, Haiti, following the 2010 earthquake, the article discusses how understandings of community resilience in action in post‐disaster developmental contexts could contribute to enhancing institutional responses under uncertainty.
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.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.033 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".