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Record W4207069854 · doi:10.5430/wjel.v12n1p115

Multimodal Texts of Political Print Advertisements in Ukraine

2022· article· en· W4207069854 on OpenAlex

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueWorld Journal of English Language · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLanguage, Communication, and Linguistic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSloganPoliticsRepresentation (politics)EmblemAdvertisingLinguisticsUtterancePictogramPsychologyPolitical scienceSociologyMedia studiesLawLiteratureArtBusiness

Abstract

fetched live from OpenAlex

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.

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 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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.322
Threshold uncertainty score0.767

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
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)0.0010.000

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 head0.014
GPT teacher head0.316
Teacher spread0.302 · 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