Enhancing Resilience from Pandemics in Urban and Rural Settlements of Nakhon Ratchasima Province, Northeastern Thailand
Bibliographic record
Abstract
The COVID-19 pandemic has highlighted the vulnerabilities of urban systems during health crises, particularly in smaller cities and rural communities where resilience strategies remain underexplored.This study addresses this gap by examining urban and rural responses in Nakhon Ratchasima Province, Thailand, providing a comprehensive perspective on pandemic resilience strategies tailored to diverse environmental contexts and demographic setups.The study aims to identify key factors enhancing resilience and to assess the effectiveness of different strategies across these varied settings.Findings reveal significant differences in resilience capacities: urban and rural settings benefit from diverse occupational structures and infrastructures, supporting dynamic pandemic responses; rural areas leverage strong community bonds but are constrained by resource limitations.This underscores the need for context-specific strategies that integrate social capital into resilience planning, emphasizing community initiatives and participatory governance.However, the study acknowledges limitations, primarily its focus on COVID-19 and challenges in defining urban systems' parameters, suggesting that future research should expand to include a broader spectrum of pandemics and disasters.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".