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Record W4384827703 · doi:10.30965/23761202-bja10023

Metro Girl: The Alliance of Art and Architecture in the Visual Aesthetics of the Modern Georgian City of the 1960s

2023· article· en· W4384827703 on OpenAlexafffund
Paul Manning

Bibliographic record

VenueCaucasus Survey · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicArt, Politics, and Modernism
Canadian institutionsTrent University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsGeorgianArchitectureAestheticsFace (sociological concept)CityscapeGirlVisual artsArtSociologySocial sciencePhilosophyPsychology

Abstract

fetched live from OpenAlex

Abstract This article explores the way a new specifically Georgian post-Stalinist art form ( cheduroba , metal engraving) that emerged in the 1960s became a commonly encountered emblematic feature of the equally new urban spaces of the rapidly changing modernist Tbilisi cityscape. This new, and yet seemingly old, art form, which frequently featured the face of a traditional Georgian girl, usually a Khevsur girl from the mountains, came to be a diagnostic part of the modern urban assemblage of Tbilisi in the 1960s. This traditional face from the Georgian past became a paradoxical figure for the alliance of art and architecture in the visual aesthetics of the modern Georgian city in the 1960s, and the art form became the stereotypical “face” of a specifically Georgian post-Stalinist “traditional-modernist” public urban art. This art form soon became diagnostic of modern Georgian urban spaces, like the Tbilisi metro, which also opened around the same time in the 1960s. The recurrent distribution of this face across newly-created modernist urban spaces together formed a Georgian version of “socialist modernism,” producing a visually-experienced “brand of socialism” for urban spaces, a procession of images traditional or national (in style or theme) in socialist modernist spaces connecting the traditional architecture of the city to the modernist architectural spaces of the new socialist city of the 1960s.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score0.929

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 studies0.0000.001
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 head0.079
GPT teacher head0.282
Teacher spread0.203 · 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 designObservational
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
Published2023
Admission routes2
Has abstractyes

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