Does Community Support in Moderating Roles Mitigate Short Comings in Rural Tourism Development?
Bibliographic record
Abstract
Background: Rural tourism offers unique experiences and the potential to revitalize local economies. Objectives: This study explores the connection between tourism destination competitiveness, hard services (e.g., infrastructure), soft services (e.g., hospitality), and the moderating role of community support within the Sundarbans National Park, rural destination in West Bengal, India. Methodology: A quantitative approach was used to measure and analyse numerical data, identifying relationships and drawing conclusions. This study employed this approach to examine how community support influences rural tourism development. The primary data for this study was gathered through a survey conducted among domestic tourists who had visited the Sundarbans. This survey offered direct insights into the experiences and opinions of these tourists. SEM is used as it handles complex relationships between variables, i.e., community support and rural tourism development. Path analysis was conducted using PLS-4 to examine how various forms of community support, such as hospitality, cooperations, etc. influence the development of tourism in Sundarbans. Results: Results underscores the importance of hard and soft services combined key drivers of rural tourism destination competitiveness setting. Moreover, strong community support significantly amplifies the positive effect of these services on the competitiveness of the destination. Conclusion: The study offers valuable insights for rural tourism development, emphasizing the significance of community connection in enhancing destination attractiveness and competitiveness.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".