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Record W2592343228 · doi:10.21226/t26880

Virtual Warfare: Masculinity, Sexuality, and Propaganda in the Russo-Ukrainian War

2017· article· en· W2592343228 on OpenAlexaffvenue
Maryna Romanets

Bibliographic record

VenueEast/West Journal of Ukrainian Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsMasculinityGender studiesSociologyHuman sexualityPoliticsIdeologyHegemonyMilitarismRhetoricUkrainianFemininityPower (physics)Russian culturePublic spaceOffensiveMainstreamState (computer science)Political scienceLawLiterature

Abstract

fetched live from OpenAlex

This paper explores the intersection of sexual and political discourses as a particular aspect of the body politics that Russia has employed, among other strategies, in its massive propaganda offensive during the hybrid warfare against Ukraine.While recognizing sexuality as one of the mainstream concepts in political analysis, the paper draws on sexually explicit imagery and idiom used in Russian social media, and public discursive space in general, as propaganda techniques, and maps their “genealogy” within wider sociocultural and political contexts. Being conceptualized in terms of Russian hegemonic masculinity in relation to subordinated femininity and non-hegemonic masculinities of its adversarial others, these setups reveal how sexuality constitutes uneven and contradictory nexuses of power once being co-opted by Putin’s propaganda machine. It is noteworthy that Russia’s neo-imperial discursive tactics of homologizing sexual and political dominance—when supplemented with the official rhetoric of restituting Russia as a great power, Orthodox Christian fundamentalism as an integral part of Russian unique “state-civilization,” state-sanctioned homophobia, and traditional macho gender ideology—contribute quite effectively to sustaining public support in Russia for aggression against Ukraine in the process of Russian reimperialization of the former Soviet space.

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)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.497
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.115
GPT teacher head0.384
Teacher spread0.269 · 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.

Study designQualitative
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

Citations18
Published2017
Admission routes2
Has abstractyes

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