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Record W4400321587 · doi:10.1080/00085006.2024.2360530

The role of popular music in Montenegro’s national propaganda: from referendum to the “Litije” movement (2006–20)

2024· article· en· W4400321587 on OpenAlexvenueno aff
Božena Miljić

Bibliographic record

VenueCanadian Slavonic Papers · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicBalkans: History, Politics, Society
Canadian institutionsnot available
Fundersnot available
KeywordsMontenegroReferendumMovement (music)Political scienceMedia studiesAdvertisingArtAestheticsHistorySociologyLawBusinessAncient history

Abstract

fetched live from OpenAlex

This article explores the uses of popular music as a propaganda tool during political events in Montenegro’s recent past. The ruling Democratic Party of Socialists (DPS) employed music as part of their propaganda campaign during the 2006 referendum to gain support for Montenegro’s independence. Patriotic songs, disseminated through state-controlled media and paid advertisements, celebrated a modern, multicultural, independent Montenegrin state. However, after the referendum, important political issues remained unresolved (especially the role of the Serbian Orthodox Church and Serbian–Montenegrin relations in general). Tensions over these culminated after the adoption of the Law on Freedom of Religion at the very end of 2019, which triggered protests throughout Montenegro known as the “Litije.” In 2020, with the ample assistance of the Serbian Orthodox Church, the opposition won the parliamentary elections that ended the rule of the DPS. The propaganda that surrounded the Litije movement relied on music as an essential medium. This time, it conveyed messages calling for a stronger role of the Serbian Orthodox Church in Montenegro and closer political ties with Serbia. By analyzing both periods’ most influential popular songs, the author highlights the intricate interplay between music, popular culture, national narratives, and the ever-evolving political climate in Montenegro.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.852
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.021
GPT teacher head0.251
Teacher spread0.230 · 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 designNot applicable
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

Citations1
Published2024
Admission routes1
Has abstractyes

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