Linguistic Landscape in the Tourist Area, Lembang Bandung, Indonesia
Bibliographic record
Abstract
This study investigates the linguistic landscape of Lembang, Bandung, Indonesia. The field site is situated in a region distinguished by the longstanding presence of diverse cultures and a high degree of multilingualism. This study employed photographs as the primary data source to gather samples from the tourist area of Lembang. The research methodology employed in this study involved the use of photographs. The data presented demonstrate the presence of multiple dimensions that characterize the linguistic landscape of a tourist area. The 83 photographs reveal the language distribution of the Lembang LL. There are three public signs discernible from the language usage of LL signs: monolingual, bilingual, and multilingual. The linguistic landscape in Lembang LL demonstrates a complex interplay of sociopolitical and economic factors, resulting in a multifaceted phenomenon. The results indicate that Indonesian, as the sole national and official language of the country, occupies a prominent position on all signs. English is recognized as the second most significant language in Indonesia, following Indonesian, and is widely used as a means of international communication. Nevertheless, it is regrettable that Sundanese is not predominantly utilized, particularly within the economic realm or business world in the Lembang region. The aforementioned findings indicate that English and Indonesian in Lembang possess both symbolic and commercial significance. Furthermore, the linguistic landscape (LL) in Lembang progressively transforms into a commodified entity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".