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Record W4392850427 · doi:10.4324/9781003450535-8

The Politics of Landscape Painting

2024· book-chapter· en· W4392850427 on OpenAlexaboutno aff
Shuyu Kong

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldArts and Humanities
TopicArt, Politics, and Modernism
Canadian institutionsnot available
Fundersnot available
KeywordsPaintingPoliticsLandscape paintingArtVisual artsPolitical scienceLaw

Abstract

fetched live from OpenAlex

This chapter explores two of the last foreign exhibitions to be held in the Mao period and one from the immediate post-Mao years: landscape exhibitions from Australia, Canada, and France held between 1975 and 1978. As cultural diplomacy exhibitions, they were all government-sponsored initiatives, and they fulfilled a clear instrumental diplomatic or political purpose at a time when China was seeking to rebuild relationships with Western countries after decades of isolation and hostility. Yet while these exhibitions were all professionally curated, including some of the best paintings representing their countries’ national artistic achievements, the reception and impact of the first two were heavily constrained by the rigid political and cultural context of their transitional time at the tail end of the ten-year Cultural Revolution. By contrast, the blockbuster French exhibition was held at the beginning of the reform and opening-up era. Comparing these three exhibitions thus makes possible analysis of the under-researched topic of global artistic exchanges with socialist China during this period of transition, focusing on why landscape painting was chosen as the preferred theme at this historical juncture, and what kinds of “decadent capitalist artworks” were considered permissible to display when the socialist door gradually started to open.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.014
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.010
Scholarly communication0.0060.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0140.001

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 head0.046
GPT teacher head0.235
Teacher spread0.189 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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