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Record W2407709532 · doi:10.7202/1035774ar

Remaking a European, Post-catastrophic Atmosphere in 2000s China: Jia Zhangke’s Still Life, Iconology and Ruins

2016· article· en· W2407709532 on OpenAlexvenueno aff
Lúcia Ramos Monteiro

Bibliographic record

VenueCinémas Revue d études cinématographiques · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicContemporary art, education, critique
Canadian institutionsnot available
Fundersnot available
KeywordsTemporalityFraming (construction)ChinaIconologyArtArt historyGeographyArchaeologyPhilosophy

Abstract

fetched live from OpenAlex

Jia Zhangke’s Still Life ( Sānxiá hǎorén , 2006) was shot in Fengjie, shortly before its flooding brought about by the construction of the Three Gorges Dam, the world’s largest hydropower station in terms of capacity. The film remakes the post-apocalyptic atmosphere found in European films made after the Second World War. From a web of cinephilic, intermedial and intertextual references, which inscribes Still Life in a local and global history of art and of film, this text compares the way Jia films his characters in a disappearing Fengjie with sequences from Roberto Rossellini’s Germany Year Zero ( Germania anno zero , 1948) and Michelangelo Antonioni’s Red Desert ( Il deserto rosso , 1964). While remaking the composition of ruins framing Edmund, in the first case, and in the second, a complex relation between background and figure in a deserted industrial landscape, Still Life creates a strange temporality, combining the imminence of a future catastrophe with the memory of past ones.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.129

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 studies0.0080.007
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.273
Teacher spread0.252 · 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
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

Citations2
Published2016
Admission routes1
Has abstractyes

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