Linguistic Landscape in Promotion of Language Through Traffic Signboards: An Introduction to the Signs in Pakistani Roads and Highways
Bibliographic record
Abstract
This present research is about how the linguistic landscape brings about the promotion of language and public awareness. The linguistic landscape is a sociolinguistic phenomenon that is used for the promotion of language and culture as well. Linguistic landscape can be seen everywhere in the society as in advertisement billboards, traffic signboards, public awareness messages on the signboards, buildings, shopping centers, airports, etc. This study covers the dimension of traffic signboards and how they are consciously or unconsciously are promoting the language in Pakistan, especially the Urdu, the National language of Pakistan, English, the international language, the Mandarin, the language of China, and the Sign language. Two hundred and ten traffic signboards are selected for the completion of this research. The data collected from the traffic signboards of the motorways, highways of all the provinces of Pakistan. The purposive sampling technique is used for this study. This study will disclose how traffic signboards are promoting the language written over them, whether written or sing language. The data was analyzed through observation with the pictures of traffic signboards. The present study is qualitative in nature, so the data is analyzed and interpreted in the description form.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".