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
Bibliographic record
Abstract
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.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".