MétaCan
Menu
Back to cohort
Record W4389987482 · doi:10.1525/cpcs.2023.2005360

The Digital Contestation of Racialized Nationhood in Russia

2023· article· en· W4389987482 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.

Bibliographic record

VenueCommunist and Post-Communist Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicPopulism, Right-Wing Movements
Canadian institutionsCarleton University
Fundersnot available
KeywordsCONTESTNegotiationCitizenshipGender studiesSociologyState (computer science)Political scienceMedia studiesPoliticsLaw

Abstract

fetched live from OpenAlex

This article offers an account of how digital-media communication enables the negotiation of nationhood from the bottom up. It explains how conservative understandings of national belonging can be challenged and co-constructed in the process of public communication over a given discursive event. Using a discourse-historical approach and multimodal critical discourse analysis focused on Manizha’s performance on the Eurovision Song Contest, the author shows the role of race, gender, citizenship, and origins for the construction of a sense of national belonging in the Russian Federation right on the eve of the full-scale war with Ukraine. The author argues that despite the commonly shared racialized understanding of Russian nationhood and the state-imposed conservative values that shape it, there was a dynamic toward a more inclusive understanding of national belonging that was advanced by some popular celebrities and picked up, bottom-up, by minority groups and liberal-minded RUnetizens.

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)0.001
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 score0.218
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.386
Teacher spread0.331 · 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