China’s red tourism: communist heritage, politics and identity in a party-state
Bibliographic record
Abstract
Purpose The purpose of this paper is to describe and evaluate red tourism in China and, in doing so, shed light on the complex relationships between tourism, heritage and identity politics. Design/methodology/approach Mixed methods – literature review, document analysis, interviews with government officials, travel agents and tourists. Findings Red tourism is an initiative to preserve, promote and pass down China’s communist past that is underpinned by political purposes. It has resulted in an imbalance between the government’s designation of communist heritage sites all over the country and the concentration of visitors in a small number of popular destinations. Red tourism fosters allegiance to the Communist Party of China. At the same time, it is expected to bring economic opportunities to remote locations through tourism spending and the branding opportunities that it provides. However, a different emphasis can be discerned at the national and local levels, whereby the former emphasizes political cohesion and the latter stresses local economic development. Research limitations/implications Four sites are investigated in detail out of the hundreds that might have been explored. Practical implications Recommendations are made to: diversify the product, increase stakeholder involvement, enhance heritage conservation plans, improve interpretation. Social implications Many implications for relationships between governments at all levels and the Chinese population. Also implications for the economic well-being of places and people adjacent to red tourism sites. Originality/value One of very few papers in either English or Chinese that addresses the red tourism policy in detail and with substantial empirical materials.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".