Social Capital and Community Adaptation to the COVID-19 Pandemic (Empirical Evidence: Sambirejo Village, Special Region of Yogyakarta, Indonesia)
Bibliographic record
Abstract
With the ever-increasing uncertainty of the impact of humans on the environment, the study of adaptive societal behavior has gained interest in seeking to actively limit disaster-related losses. Despite numerous studies on the role of social capital in Indonesian tourism, the extent to which community social capital adapts to social order changes due to events like the COVID-19 pandemic or earthquake shocks has not been thoroughly studied. This study explored the social capital of people in tourist village areas, specifically in Sambirejo Village, Indonesia, and how it supported collective action during the COVID-19 pandemic to enhance community resilience and in turn succeed as a tourist village. Sambirejo Village has been severely impacted by the COVID-19 pandemic, resulting in a decline in tourism visits and income, highlighting the importance of social capital in fostering resilience. The research utilized a quantitative approach, collecting data through a questionnaire and analyzing descriptive statistical results. The model construct was then built and tested using a Structural Equation Modeling (SEM) analysis. The SEM analysis revealed the crucial role of government and community initiatives in fostering community resilience during the COVID-19 pandemic, emphasizing the need for well-placed policies to help communities increase their social capital and combat the pandemic effectively.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".