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Record W2572346261 · doi:10.3968/6824

Linguistic Landscape as a Tool for Promoting Sales: A Study of Three Selected Markets in Ibadan, South West Nigeria

2016· article· en· W2572346261 on OpenAlexvenueno aff
Joshua Sunday Ayantayo

Bibliographic record

VenueStudies in literature and language · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicLinguistics, Language Diversity, and Identity
Canadian institutionsnot available
Fundersnot available
KeywordsPhenomenonWork (physics)MarketingSign (mathematics)BusinessSociologyAdvertisingEngineering

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.263
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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