Multimodal Texts of Political Print Advertisements in Ukraine
Bibliographic record
Abstract
Political print advertisements as an integral part of political propaganda contribute to creating a positive image of a politician or political party aiming to win popular votes. The effective combination of verbal and non-verbal (pictorial) components presupposes a purposeful influence on the electorate by means of activating existing socio-cultural schemas among voters. This study aims to analyze the generic structural potential of political print advertisements in Ukraine and to interpret the symbolic representation of the verbal and non-verbal levels of political advertisements. The data, encompassing 25 leaflets, was collected in 2020 during the local election campaign in Chernivtsi, Ukraine. The analysis has been conducted on two levels: literal and interpretative. Following the principles of Generic Structure Potential of advertisements (Cheong, 2004) we have outlined the GSP of print political advertisements in Ukraine. Thus, non-verbal (visual) components incorporate the lead (Locus of Attention (LoA) and Complement to the Locus of Attention (Comp.LoA) emblem, pictogram, verbal (linguistic) components involve Announcement (Opening), Enhancer, Slogan, and Call-and-visit Information. The next level of the analysis presupposed the interpretation of the obtained results. In accordance with this, the symbolic representation of the verbal level of political advertisements, incorporating patriotic, social, economic, and political slogans has been revealed. In terms of the symbolic representation of the non-verbal level of political advertisements, we observe the following in the analyzed material: the photo of the candidate, appearance, pictograms, and additional pictorial elements. The results indicate that the displays of the analyzed material are often congruent and explicit. Cases of incongruent and implicit displays have been observed as well. Such advertisements boost viewers’ curiosity as they contain less verbal information. The results of the election show that the candidates whose advertisements incorporated symbolism and greater non-verbal information, led among the popular votes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".