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LOST ARCHITECTURE MONUMENTS: THE CASE OF BERLIN RETHINKING HISTORY

2019· article· en· W4321269559 on OpenAlexaboutno aff
R. SHISHKIN

Bibliographic record

VenueUrbanizm · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsnot available
Fundersnot available
KeywordsArtifact (error)ArchitectureContext (archaeology)ChapelQuarter (Canadian coin)HistoryCultural heritageFace (sociological concept)Class (philosophy)History of architectureValue (mathematics)Historic siteVisual artsSociologyArt historyArchaeologySocial scienceArtComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Specialists often face various social and ethical challenges when reconstructing an architectural monument, historical building, or iconic artifact. Berlin is a city with a complex past, therefore for many years a number of historical objects have been constantly disputed over their value and need for protection. The Berlin projects in the last decades are largely illustrative examples of different approaches to their equivocal heritage, due to large- scale reconstruction activities carried out with the involvement of world-class architects.The following works are considered: Nikolaiviertel (Nicholas' Quarter), Reichstag building, Kaiser Wilhelm Memorial Church Gedächtniskirche, Berlin Wall and Chapel of Reconciliation. The purpose of this paper is to review general trends that are true for a number of projects in the city, diverse in their use of reconstruction methods, but combined with the complexity of historical and ethical factors. The result of this review is the conclusion about the emotional impact felt by the viewer in the modern context as the primary goal of the reconstruction of such objects. The role of public opinion in making design decisions is emphasized.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.830
Threshold uncertainty score0.992

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.000

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 head0.065
GPT teacher head0.206
Teacher spread0.141 · 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.

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

Citations0
Published2019
Admission routes1
Has abstractyes

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