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Record W2763086522 · doi:10.2298/muz1722039m

Years of sound living: Mikser festival in Savamala (2012-2016)

2017· article· en· W2763086522 on OpenAlexaboutno aff
Ivana Medić

Bibliographic record

VenueMuzikologija · 2017
Typearticle
Languageen
FieldPsychology
TopicNostalgia and Consumer Behavior
Canadian institutionsnot available
FundersSerbian Academy of Sciences and ArtsMinistarstvo Prosvete, Nauke i Tehnološkog RazvojaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsNightlifeSoundscapeTourismCreativityQuarter (Canadian coin)Creative economyExhibitionHistoryVisual artsSound (geography)EconomyPolitical scienceSociologyAestheticsArtLawArchaeologyEconomics

Abstract

fetched live from OpenAlex

This article deals with the soundscape of Mikser, an independent festival of contemporary creativity, established in 2009 in Belgrade, the capital city of Serbia. I focus on the years 2012-2016, during which Mikser was taking place in Savamala, an urban quarter in central Belgrade - which itself has undergone various urbanistic and cultural transformations in recent years. The creative team behind Mikser aimed to turn Savamala into a permanent fixture on the map of Belgrade nightlife and a tourist hotspot; the fact that they did not succeed was on account both of financial issues and conflicting top-down business interests. My conclusion is that the long-term survival of the festival is not dependent on its program or audiences, but on securing official support and infrastructure.

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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.002
Scholarly communication0.0040.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.058
GPT teacher head0.353
Teacher spread0.295 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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