The Linguistic Landscape of China: A Field-Based Study of an Ancient Town’s Historical Precinct
Bibliographic record
Abstract
This study comprises an empirical LL case study in Datong, an ancient town of China. Specifically, it focuses on the top-down (government) and bottom-up (commercial and other) linguistic landscape of historic Lanxi Street as its research setting. The paper draws on theories of LL studies to examine the use of written language on signs in the public area of a historical street. The study analyzed the differences between official signs and unofficial signs, and to identify characteristics that might be specific to a historic precinct. The researchers compared the linguistic landscape of Lanxi Old Street with an urban commercial street in Tongling City, to examine the differences and their causes between a small, heritage town and a city. Based on a mixed-methods research design, the data of this paper incorporate photographs, interviews and questionnaires, which were analyzed with the help of SPSS. This empirical study sheds light on protect heritage LL to preserve its own characteristics against the flow of globalization and the worldwide domination of English in the context of a sociolinguistics approach.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".