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Record W4285489591 · doi:10.1515/9789048557110-015

14. Lessons from settler colonies and the restitution of Nazi-Looted art

2022· book-chapter· en· W4285489591 on OpenAlexaboutno aff
Jos van Beurden

Bibliographic record

VenueAmsterdam University Press eBooks · 2022
Typebook-chapter
Languageen
FieldEarth and Planetary Sciences
TopicArchaeological Research and Protection
Canadian institutionsnot available
Fundersnot available
KeywordsRestitutionNazismArtArchaeologyHistoryLawPolitical science

Abstract

fetched live from OpenAlex

Towards a Ne w E thic s A t three major moments in the history of art robbery, Western and Southern Europe have had the leading roles.The first was in Europe's colonial territories, in the so-called 'distant' or 'imperial' colonies, that is, far-off territories in Latin America, Asia and Africa.This was the subject of the previous chapters.The second was in settler colonies in South Africa, the United States, Canada, Australia and New Zealand.Settlers were Europeans who had left their continent for good and cut administrative ties with their mother country.They conquered territories, enslaved the peoples who had lived there since time immemorial and confiscated fertile and mineral-rich lands.As in the distant colonies, the newcomers in settler colonies appropriated cultural artefacts and ancestral remains on a large scale.Most are still in museums and private collections in North America, South Africa, Australia and New Zealand, but museums and private owners in Europe also possess many of them.The third moment took place under the Nazi regime.You come across stories about it, for example, from the descendants of art dealer Jacques Goudstikker and collectors such as Franz Wilhelm Koenigs, Baron Mór Lipót Herzog or Maria Altmann, who all reclaimed looted works of art.Sometimes, this moment comes even closer to home, as it does in the story of a Jewish family who lived in my street.In the Second World War, the father and a son perished in Auschwitz.The mother and three other children survived.Their next of kin have been trying to get compensation for all the valuables that disappeared from their house.For a long time, the three moments were viewed as separate events.Nowadays, they are more often linked.What they have in common is

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: Other · Consensus signal: Other
Teacher disagreement score0.969
Threshold uncertainty score1.000

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.217
Teacher spread0.179 · 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
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
Published2022
Admission routes1
Has abstractyes

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