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Record W4245540531 · doi:10.22215/rera.v3i1.182

Propaganda Instruments in Contemporary Campaigns: Comparison of Estonian Political Television Advertisements and Modern Television Commercials

2007· article· en· W4245540531 on OpenAlexvenueno aff
Agu Uudelepp

Bibliographic record

VenueReview of European and Russian Affairs · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicMedia Studies and Communication
Canadian institutionsnot available
Fundersnot available
KeywordsEstonianPoliticsAdvertisingTelevision studiesIdeologyMedia studiesDemocracyPolitical scienceSociologyLawBusiness

Abstract

fetched live from OpenAlex

The author argues in the present article that although propaganda is considered mostly a tool of ideological communication suitable for use during wars or in totalitarian states, it is still used in contemporary democratic societies at peacetime and there are no major differences between employing instruments of propaganda in the public or the private sectors. The present analysis is based on the similarities and differences between Estonian political television advertisements and modern television commercials with an emphasis on the application of propaganda instruments. The author employed content analysis when studying the sample in which were 100 non-political and 84 political advertisements. This research shows that Estonian political television advertisements and international non-political television advertisements share some significant similarities: cognitive propaganda instruments are more widely employed than social ortechnological ones. The role oftechnological propaganda instruments is diminishing and such instruments are replaced by structural ones. A major difference is that, on average, there are more propaganda instruments per advertisement in political television advertisements than in non-political television advertisements, and technological propaganda instruments are not employed in non-political television advertisements.

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.

How this classification was reachedexpand

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.763
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.055
GPT teacher head0.364
Teacher spread0.309 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2007
Admission routes1
Has abstractyes

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