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Record W4389510289 · doi:10.1386/jcca_00086_1

Digital China and its discontents: On the politics of Sinofuturism and image building at the Venice Biennale

2023· article· en· W4389510289 on OpenAlexaff
Gigi Wai-Chi Wong

Bibliographic record

VenueJournal of Contemporary Chinese Art · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicChinese history and philosophy
Canadian institutionsWestern University
Fundersnot available
KeywordsPavilionChinaChinese artPoliticsContemporary artAestheticsRhetoricSituatedSociologyCivilizationVisual artsHistoryArtArt historyPolitical scienceLawComputer scienceLinguisticsPerformance artPhilosophy

Abstract

fetched live from OpenAlex

This article examines how the China Pavilion at the 2022 Venice Biennale attempts to reimagine a new ontology between contemporary Chinese art, artificial intelligence-generated art and artistic practices, as well as the official Chinese discourse on technological positivity. It argues that the China Pavilion can be read through the lens of a Sinofuturist discourse and how the pavilion is spatially and temporally situated in contemporary digital Chinese art. Taking the title ‘Meta-Scape’, the China Pavilion can be understood as a futuristic phototype that the Chinese state mobilizes in formulating a rhetoric of a cohesive digital civilization. This underlines the ways the pavilion not only generates technological inquiries to imagine new paths for artistic practices but also manifests the role that Chinese new media art has on rendering the nation’s international image. In exploring one of the AI-generated artworks titled Streaming Stillness (2022), this article investigates how the ‘techno-turn’ in contemporary Chinese art illuminates the digitalization of cultural memory in relation to the dynamics and discontents between technological aestheticism and China’s national image building process.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score0.506

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.045
GPT teacher head0.287
Teacher spread0.243 · 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 designTheoretical or conceptual
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

Citations0
Published2023
Admission routes1
Has abstractyes

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