Cultural Tourism as a Catalyst for Rural Development: A Spatial-Econometric Study of a Tourism Village in North Sumatra, Indonesia
Bibliographic record
Abstract
Cultural tourism is increasingly viewed as a strategic pathway for inclusive rural development, particularly in culturally rich yet economically underdeveloped regions.This study examines its role through an integrated spatial and econometric framework, focusing on Meat Tourism Village in North Sumatra, Indonesia.Despite its policy recognition and cultural assets, the village has lacked empirical evaluation of tourism's development outcomes.Data were collected through structured surveys with 300 domestic and international tourists conducted between June and September 2024, and supplemented by stakeholder interviews.Geographic Information Systems (GIS) were used to identify tourism clusters, while multiple linear regression and path analysis assessed the effects of tourist behavior on income, employment, and infrastructure, with community participation as a mediating variable.Results reveal that tourist expenditure, length of stay, and cultural product consumption significantly predict household income growth.Spatial analysis highlights distinct cultural nodes as economic hotspots.Path analysis confirms that community participation amplifies employment outcomes, underscoring the value of inclusive governance.The study contributes a replicable methodological model integrating spatial diagnostics and econometric evaluation for rural cultural tourism.These findings underscore the transformative potential of cultural tourism when embedded within participatory and place-based development frameworks.
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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".