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Record W4388636739 · doi:10.5539/ach.v15n2p54

Artistic Intervention as Scene Construction/Deconstruction: An Analysis of the Display of the Kangxi Throne in the Humboldt Forum in the Context of the Cross-Cultural Exhibition

2023· article· en· W4388636739 on OpenAlexvenueno aff
Fan Zhang

Bibliographic record

VenueAsian Culture and History · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsnot available
Fundersnot available
KeywordsDeconstruction (building)ExhibitionThroneContext (archaeology)Visual artsSociologyIntervention (counseling)Cultural historyAestheticsHistoryArtPoliticsAnthropologyArchaeologyPsychologyLawPolitical scienceEngineering

Abstract

fetched live from OpenAlex

Based on the research perspective of cross-cultural exhibition analysis, this paper takes case study as the fundamental methodology under the framework of museology and art history research in order to analyse the new display of the Kangxi throne and its screen, which were lost overseas from China during the war years and have been transferred from the Museum of Asian Art in Dahlem to the Humboldt Forum, which is deeply involved in the controversy of its colonial history. This study primarily focuses on the situational methods in which the exhibits were connected to the public under artistic intervention. In the exploratory stage, the “Game of Thrones” project in the Humboldt Lab Dahlem programme offered multiple versions of interpretations, which ultimately prompted the museum to change the scene restoration plan and invite the famous Chinese architect Wang Shu to complete the rooftop installation. Between construction and deconstruction, the artwork’s scenic intervention creates structural descriptions for the presentation of cross-cultural differences, and accommodates ethical conflicts from different sides with its poetic, distanced interpretations, while providing a shared language for comprehending the various facets of nationhood amidst the intersection of history and the present.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.656
Threshold uncertainty score0.959

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.041
GPT teacher head0.265
Teacher spread0.224 · 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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