The Power of Folk Music: City Branding, Musical Imaginaries, and Tourism-induced Placemaking in Yulin, Chengdu (China)
Bibliographic record
Abstract
As intercity competition increases, cities search for distinctive meanings and create identifiable symbols for places to increase their attractiveness. These symbols change the urban landscape and shift the characteristics of ordinary neighborhoods. This paper is a case study of a neighborhood called Yulin in Chengdu in southwest China. Developed in the 1980s, Yulin was an ordinary neighborhood representing Chengdu’s work unit housing in the 1980s and 90s. Although its name is well known by locals, Yulin was suddenly exposed to the wider public of the country due to a folk song. In 2017, a folk musician, Zhao Lei, performed his song, Chengdu, on Hunan TV. Using guitar, piano, and children’s voices, the song depicts a romantic story on the street of Yulin and reinforces the imaginaries of Chengdu as a leisure city. Names and addresses in the lyrics became well-known overnight. From then on, tourism-induced investments have driven significant changes in Yulin. This study focuses on the built environment of Yulin and shows the power of folk music to shift people, buildings, and money. It discusses how a contemporary folk song resonates with the imaginaries of Chengdu as “the city of leisure” and how Yulin has been changed due to musical imaginaries. In addition, it aims to enrich the discourse of city branding and placemaking to raise awareness of tourism-induced changes in ordinary neighborhoods, in China and beyond.
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 | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".