Conference Report: German-American Encounters After World War II and the Holocaust. Conference at the GHI, September 26-28, 2002
Bibliographic record
Abstract
Conference at the German Historical Institute, September 26–28, 2002. Conveners: Alexander Freund (GHI/AICGS/University of Winnipeg), Atina Grossmann (Cooper Union), Raimund Lammersdorf (GHI), Annette Puckhaber (GHI). Participants: Keith Allen (Corcoran College of Art and Design), Tobias Brinkmann (Simon-Dubnow-Institut für Jüdische Geschichte und Kultur, Leipzig), Gerhard Fürmetz (Bayerisches Hauptstaatsarchiv, Munich), Laura J. Hilton (Muskingum College, New Concord, OH), Robert Gerald Livingston (GHI), Jeffrey M. Peck (AICGS/Canadian Centre for German and European Studies, Toronto), Johannes Platz (University of Trier), Steven Paul Remy (Brooklyn College—CUNY), Nils H. Roemer (University of Southampton), Timothy L. Schroer (George Mason University), Michael Schüring (Max-Planck-Gesellschaft zur Fo¨rderung der Wissenschaften e.V., Berlin). Additional Participants: Kai Behrens (GHI), Caroline Galm (GHI), Janel B. Galvanek (GHI), Anna Held (GHI), Jeffrey Herf (University of Maryland), Dirk Schumann (GHI), Christoph Strupp (GHI), Birgit Zischke (World Bank, Washington, DC).
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.073 | 0.009 |
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".