Social Capital and Quality of Life in an Indonesian Rural Tourism Village
Bibliographic record
Abstract
Optimal quality of life (QoL) requires support from various forms of capital, including social capital.Although social capital is considered a relevant non-physical asset, its influence on meeting QoL needs has not been fully clarified.This research aims to fill this knowledge gap by examining the relationship between social capital and QoL in the Sidomulyo Tourism Village, a tourism village based on ornamental plant cultivation in Indonesia.This research is important because research related to social capital and QoL is still limited.Apart from that, this research also understands how social capital can be implemented to improve community QoL.Data was collected through distributing questionnaires to 307 respondents (Head of Household).Data analysis was carried out using SEM-PLS analysis with two stages, namely outer model and inner model analysis.Research findings show that social capital as a whole has a significant influence on the QoL of the Sidomulyo Tourism Village community.Furthermore, elements of social capital, especially trust, have the strongest impact on QoL elements, especially material aspects of well-being.The implications of these findings provide valuable insights in developing policies and intervention programs to improve QoL in rural communities, especially those related to strengthening social capital.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".