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Record W2984642302 · doi:10.1177/0010414019879949

The Co-optation of Dissent in Hybrid States: Post-Soviet Graffiti in Moscow

2019· article· en· W2984642302 on OpenAlexfundno aff
Alexis Lerner

Bibliographic record

VenueComparative Political Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Spaces through Art
Canadian institutionsnot available
FundersUniversity of TorontoCosmos Club FoundationGeorgetown University
KeywordsDissentGraffitiLegitimacyPolitical dissentPoliticsAccountabilityState (computer science)Political sciencePower (physics)Public administrationSociologyMedia studiesLawPolitical economyVisual arts

Abstract

fetched live from OpenAlex

Hybrid leaders seek job security. To stay in power, it may be intuitive that they respond to dissent with a heavy hand. However, these leaders are subject to accountability and concerned with legitimacy and therefore must consider the optics of their decisions. By co-opting a previously independent avenue of communication and its leadership, the state eliminates challengers, curates its public image through trusted social leaders, and reinforces control without resorting to repressive methods that may backfire. Based on a decade of fieldwork, data collection, and expert interviews, I evidence the co-optation of dissent via thematic, spatial, and material shifts in political public art, crafted between the 2012 and 2018 Russian presidential elections. As it consolidated power during this time, the Putin administration co-opted critical graffiti artists and flooded out those unwilling to cooperate, replacing subversive and anonymous anti-regime graffiti with Kremlin-curated murals, particularly in the city center.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.009
Scholarly communication0.0030.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.083
GPT teacher head0.451
Teacher spread0.368 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations35
Published2019
Admission routes1
Has abstractyes

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