Linguistic Landscape as a Tool for Promoting Sales: A Study of Three Selected Markets in Ibadan, South West Nigeria
Bibliographic record
Abstract
This work discusses the roles of Linguistic Landscape in promoting sales. This sociolinguistic phenomenon has been deployed knowingly or unknowingly as a marketing strategy. Marketers most especially use this tool to attract customers to buy their products. The thrust of this work is to examine how linguistic landscape is used to promote sales of goods and services. Data for this work were collected from sign posts and the billboards in the three selected markets within Ibadan metropolis (Bodija, Alesinloye and Dugbe). Fifty sign posts and fifty billboards were used for the data. People were also interviewed to know their reaction to this marketing strategy. The work reveals that people react positively to this marketing strategy because it attracts people’s attention to the products. However, not all those that were interviewed reacted positively to it. Some claimed that it is often ambiguous and makes it difficult for them to understand. This work also reveals that the use of linguistic landscape as a marketing strategy belongs to bottom-up classification. We also discovered that in an attempt to use this phenomenon as a marketing strategy, both the official and the dominant languages in the studied areas were used.
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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 | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".