Social Capital and Disaster Resilience Nexus: A Study of Flash Flood Recovery in Jeddah City
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
Governments, researchers, and humanitarian agencies have increasingly focused on reducing disaster impacts and enhancing the resilience of individuals, households, and communities, as the human and economic costs of natural disaster events have dramatically increased over the past century. Achieving resilience in a disaster context means the ability to survive future natural disasters with minimum loss of life and property as well as the ability to create a greater sense of place among residents, a stronger, more diverse economy, and a more economically integrated and diverse population. However, less attention has been paid to the significance of social capital in a post-disaster context and its contribution in building community resilience. It is very obvious that the contribution of social capital to post-disaster resilience in a Middle Eastern/Saudi Arabian context is virtually unknown. With a focus on the Saudi Arabian context, this research paper develops a social capital framework centered on resilience and post-disaster recovery. To conduct this study, a holistic approach to data collection is followed through questionnaire surveys, structured and non-structured interviews with citizens, and informal discussions with government and major stakeholders related to flash flood disaster management in the City of Jeddah. It is interesting to note that several religious institutions have played important roles in evacuating people and providing help for a quick recovery. In addition, government organizations are taking the recovery process seriously by providing necessary help in the flood-stricken areas. Within the scope of the given framework, the research explores and evaluates the role of social capital in post-disaster recovery efforts through a case study of the 2009 and 2011 Jeddah flash floods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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