Decoding ethnic tourism: a comprehensive analysis of global trends, key themes, and knowledge frameworks
Bibliographic record
Abstract
The relationship between tourism and ethnicity is long-standing, complex, and deeply interwoven. Despite growing academic and practical interest in ethnic tourism, comprehensive systematic reviews of global research remain limited. This study analyzes 963 publications on ethnic tourism from 1991 to 2023 using bibliometric and content analysis to identify research trends, keyword clusters, and themes in highly cited literature. Based on the findings, a comprehensive knowledge framework was developed, and directions for future research were proposed. Key findings include: (1) Ethnic tourism publications increased in four distinct phases, with primary contributions from scholars and journals in the United States, Australia, Canada, China(Mainland and Taiwan), and Europe. The high-attention keywords such as ‘conservation’ and ‘sustainability’, high-potential keywords like ‘impact’ and ‘culture’, and mature keywords including ‘indigenous tourism’ were identified. (2) Keyword co-occurrence analysis revealed nine major clusters, including ethnic tourism, cultural heritage, and indigenous peoples. (3) Ethnic tourism research is interdisciplinary, with strong theoretical connections between host–guest interaction and authenticity. Methodologically, the field has evolved from primarily qualitative approaches to increased use of quantitative and mixed methods. (4) A knowledge framework was constructed by synthesizing thematic literature, offering structured insights into the development of ethnic tourism research. This study contributes to understanding global research trends and provides a foundation for future theoretical and methodological advancements in the field.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.047 | 0.071 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".