A long-term tourism scheme within a networked urban regeneration strategy for historic quarters
Bibliographic record
Abstract
Many cities have witnessed the rise of urban tourism since 1970s. Nowadays, especially in developing countries, tourism has become an important industry to initiate employment, boost the economy, and regenerate urban quarters. Recently, tourism has been included into a comprehensive process of urban regeneration of historic quarters, and is becoming closely related with other urban planning strategies, such as conservation and creative industry (Tiesdell et al., 1996, Cunningham, 2002). Many historical quarters have been transformed into tourist destination with the aim of reviving the economy and enhancing urban image and culture. However, a short-sighted urban tourism scheme usually raises diverse negative issues, such as the loss of identity and social cohesion, etc. In recent decades, cities in China have experienced huge urban changes. As many urban historical quarters were demolished to facilitate rapid urban development and new city image, many old neighbourhoods were obliterated and their residents, relocated to fringe areas. This paper presents a comparative study of two cases in China— Tianzi Fang in Shanghai and the Muslim Quarter in Xi’an. It discusses the alternative approaches to develop sustainable tourism in urban context: a long-term tourism scheme with an active recycling process which not only recycles the tangible aspects but also the intangible aspects of the quarter, and that contributes to a process of sustainable transformation.
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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 | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".