Renewing Nazi-era provenance research efforts: case studies and recommendations
Bibliographic record
Abstract
This article advances our understanding of current approaches to Nazi-era provenance research in American museums by examining four active provenance programs with different structures, priorities, and funding sources. Interviews with key personnel and a close examination of internal and publicly accessible documents reveal that active Nazi-era provenance research programs share core characteristics such as supportive leadership, team-based practice, and a willingness to shape programs around particular resources and circumstances. Using these core characteristics as a model for renewed Nazi-era provenance research efforts in all museums with covered objects, the author recommends that museum leaders pursue new ways to address Nazi-era provenance research needs; that museums delegate certain Nazi-era provenance research and documentation tasks to a team of qualified, non-curatorial personnel; that Nazi-era provenance researchers establish a formal professional network to facilitate training and collaboration; and that museums explore unique funding sources for the explicit purpose of Nazi-era provenance research.
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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 | 0.112 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.026 | 0.015 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".