Zwangsarbeit im Ersten Weltkrieg: Deutsche Arbeitskraftepolitik im besetzten Polen und Litauen 1914-1918
Bibliographic record
Abstract
Recently, at a small conference in Germany focused on the Eastern Front during the First World War, I was once again reminded of just how horrified my German historian colleagues are of the dreaded C-word . . . ‘continuity’. When the C-word was even hinted at, you could see the backs stiffen and the tension rise among those in the room who had received their PhDs from German universities. This was met with bewilderment from those of us educated and based outside Germany. In the non-German academic world, if you do not make a case for continuity, connection and relevance of your small specialty to the larger trends of national or global history you are dismissed as narrow-minded, provincial and largely unhelpful. Because the Indian Wars of the American nineteenth century were in fact a part of my talk there, I will make the following point: if an American historian of the Frontier West argued to his or her colleagues that, whether due to a change in federal administrations (the American Revolution), different social and cultural atmospheres, or varying forms of warfare and treatment of Native populations (from forced population transfer to genocide), one should not speak of some forms of ‘continuity’ between the first of those dozens of wars to the last, the speaker would quite simply be mocked, and perhaps even sneered at in the hallways. This American example draws on a period of over 150 years with obviously no continuous ‘personal’ history.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".