Digital China and its discontents: On the politics of Sinofuturism and image building at the Venice Biennale
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.029 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".