Capturing Social Issues Through Signs: Linguistic Landscape in Great Malang Schools, Indonesia
Bibliographic record
Abstract
This study aims to analyze the signs associated with social issues in school spaces by using the Linguistic Landscape approach. Data were obtained from 10 public and private schools in Great Malang, Indonesia through photography. The study reports several findings, namely (1) Indonesian schools are monolingual, bilingual, and multilingual with the dominant use of Bahasa, English, Arabic and Javanese, (2) phrases and clauses dominate the appearance of data in linguistic aspects, compared to words. Therefore, they are very effective in mediating messages conveyed in signs, (3) it comprises of eight themes, namely environment, juvenile delinquency, health, discipline, motivation, attitude and behavior, religion, and nationalism, (4) there are 9 out of 18 values of character education, namely hard work, creative, discipline, national spirit, religious, honest, environmental care, reading hobby, and love for peace. In conclusion, Bahasa Indonesia is associated with the symbol of nationalism and language policy, where English, Arabic and Javanese symbolize modernization, Islam, and the local culture, respectively. Furthermore, the themes and values of character education that emerge represent the conditions of the problems faced by students. This finding suggest education through signs, evoke perceptions and attitudes which is used to strengthen character education in schools to solve social problems.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".