Linguistic Landscape and Markedness Conceptualization in Commercial Ads
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it