Chinese in the Linguistic Landscape of Chinese Communities in Malaysia: A Tale of Three Cities
Bibliographic record
Abstract
The study of linguistic landscapes (LL) in Malaysia has emerged as a prominent area of research in Asia, predominantly focused on Kuala Lumpur, the capital city. Despite Chinese constituting over one fifth of Malaysia’s population, the presence of Chinese in the LL remains insufficiently explored. This empirical study addresses this gap by examining the visibility of Chinese in the LL of Chinese communities across three Malaysian cities: Penang, Ipoh, and Johor Bahru. By analysing a corpus of photographed public signs from various spaces, this research investigates the visibility of Chinese in the LL both quantitatively and qualitatively. The findings reveal that the visibility of Chinese in the LL of Chinese communities in the three selected cities not only signifies Chinese identity but also serves as an effective means to preserve traditional Chinese culture among Malaysian Chinese. This study underscores the importance of understanding the role of minority languages in the LL within ethnic communities, contributing to a more comprehensive view of Malaysia’s multilingual and multicultural context.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".