A Digital Trail of Rupture. The German Film Exile 1933-1945 in the Data of Günter Peter Straschek
Bibliographic record
Abstract
How can digital humanities methods reveal the productivity and connectedness of a group of historical individuals linked by displacement from a country? German film exile during National Socialism, 1933-1945, has always been a complex subject to research because of the scattered nature of the sources in the international distribution of archives (Asper, Horak, Hilchenbach). It remains challenging to reconstruct the worldwide dispersed separate flight routes of more than 3000 individuals who worked for the German film industry before 1933. This contribution concerns a list of film exiles collected by the exile researcher and filmmaker Günter Peter Straschek (1942-2009), whose collection of files belongs to the _German National Library, German Exile Archive 1933-1945, Frankfurt am Main_ and was inventoried according to their Normdata. To this end, a database of GND exile data containing names, birth and death dates, professions, and countries of exile was compiled and enriched with data from online resources (Wikidata and IMDb). How can a more comprehensive look at the data reveal the devastating loss for German film (2) and, on the other hand, the collaboration on the flight (3) showing exiles remaining defiant? By incorporating new data from the Straschek Estate, digital methods further enhance historical research findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".