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Record W4312250132 · doi:10.1080/00085006.2022.2106688

Naming the war: Russian aggression in Ukrainian official discourse and mass culture

2022· article· en· W4312250132 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Slavonic Papers · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean and Russian Geopolitical Military Strategies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsUkrainianState (computer science)RhetoricMass mediaPolitical sciencePoliticsPolitical culturePopular cultureVocabularyRussian cultureMedia studiesSociologyLawLiteratureLinguistics

Abstract

fetched live from OpenAlex

This article discusses the representation of Russia’s all-out invasion of Ukraine, beginning in February 2022, in Ukrainian official discourse, media, and mass culture. A new stage in the war with Russia that has been ongoing since 2014, the invasion prompted a search for a new political and cultural vocabulary. If the Ukrainian state at first tended to use familiar Soviet models, mass culture responded with powerful memes, slogans, and images that mobilized the public in defence of Ukraine. The Zelens′kyi administration soon recognized the importance of modern mass culture and switched to borrowing successful symbols from it. In some cases, state institutions put potentially popular materials on social media to see whether they would work well and then re-appropriated them once they entered popular culture. Ultimately, it has been the test of popular culture that has determined the success or failure of official rhetoric.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.790
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
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 head0.011
GPT teacher head0.268
Teacher spread0.257 · 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