Linguistic Dimension of Political Advertising: Analysis of Linguistic Means of Manipulative Influence
Bibliographic record
Abstract
The manipulative influence of speech is a constant subject of debate. Advertising as a reflection of social reality has manipulative potential. Studies of the linguistic means that form and enable the manipulative slogans of political advertising are studied in terms of sociolinguistics, language dynamics of political discourse, and in most scientific sources are revealed as a new conscious manifestation of the desire to influence the masses. Manipulative discourse is considered in the strategic field of didactics of language and culture. Political advertising is capable of generating a huge stream of individual and collective reflections that touch upon the most basic human features of linguodidactics. Not only is the contrast between “language” (bearing, reassuring, essential) and “manipulative” (obscure, threatening, dangerous) stark, but their juxtaposition is neither more nor less unnatural. The main purpose of political discourse is to promote understanding, communication, and dialogue between cultures and to have a manipulative effect. So, the main question is whether argumentation should be considered in the analysis of the manipulative influence of political advertising discourse. Subsequent directions of consideration of the concept are determined by the possibility of using the proposed model in the study of political speeches on the material of other languages. In further scientific searches, we will focus on other theoretical studios of discourse, focusing on those of that are aimed, among other things, at the analysis of political discourse, in particular, the most significant factors determining its essence, peculiarities of construction, and development in those or other socio-cultural conditions.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".