Multi-Attributive Social Embeddedness in Cultural Tourism Development: A Case of the Osing Community in Indonesia
Bibliographic record
Abstract
A vast development of the rural tourism induces social transformations and economic enhancement.The tourism activities suggest the transformation process of how social attributes of the actors work in the social network.It is exemplified in Banyuwangi, in which the cultural festival, as the main tourism attraction in Osing Community, is manifested in the social embeddedness of the tourism actors.This article aims to explain the complexity of social embeddedness in tourism and further analyze the typology and processes of social embeddedness among tourism actors.This study employed a qualitative approach with a case study design through in-depth interviews with 29 tourism actors and semi-participatory observations.The cultural tourism activities were constructed by multiple embeddedness typologies, namely relational and structural, institutional, political, and spatial embeddedness which affect actors' rationality into more complex social actions.The process of social embeddedness was framed by social norm reference and social networks among tourism actors to sustain the tourism business and strengthen Osing ethnic identity.This study allowed for a better understanding of the complexity of embeddedness that occurs in cultural tourism in a rural setting, which is against, yet completes the original ideas of social embeddedness theory in an urban industrial context.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".