Linguistic Landscape and Markedness Conceptualization in Commercial Ads
Bibliographic record
Abstract
This research focuses on the linguistic landscape (abbreviated as LL) and explores how both linguistic and non-linguistic markedness are manipulated in commercial advertisements to create a compelling impact on potential customers, thereby attracting their attention to the product. The study utilizes data collected from commercial signs found on social media platforms and snapshots taken within the Mataram Municipal area. These gathered data are then subjected to analytical processing, taking into account the verbal and non-verbal context surrounding the advertisements. Additionally, the conceptual aspects of the speakers are also considered to support the analysis of the marked and unmarked status of the analyzed terms. The findings reveal that the exposure of markedness in the signage heavily relies on foregrounding techniques. Foregrounding is primarily achieved through the violation of the speakers' expectations regarding the terms used, encompassing both linguistic and socio-cultural perspectives that readers possess. Furthermore, these foregrounding techniques are reinforced by the proximity between the text and the surrounding context of the signage and its environment. By combining these textual and environmental elements, advertisers aim to optimize the intended message conveyed by the signage.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.000 | 0.003 |
| 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".