From Manitoba to the Memel: Max Sering, inner colonization and the German East
Bibliographic record
Abstract
Whereas most of the debate surrounding the ‘colonial roots' of the Holocaust has centred around the German genocidal campaign against the Herero in south-western Africa, a much more direct and continuous story emerges when one traces the flow of ideas from the North American western frontier to the German East. In the 1880s, the agrarian economist Max Sering travelled throughout America and Canada, and came to formulate a settlement programme modelled upon what he saw there as the answer to Germany's ‘Polish problem', and indeed to virtually all the ills of modernity. From 1886 to 1914 Sering provided the intellectual ammunition for the Prussian programme of inner colonization, the purchase of land from Poles and the settlement of German ‘colonists' in the provinces of Posen and West Prussia. During the First World War, Sering's views, along with Germany's, would radicalize, as he drew up plans for the settlement of two million Germans in Latvia. Although the Nazi biological racist Darré would reject Sering's assimilationist thinking, the ‘spatial planner' Meyer would see to it that the legacy of a German way of seeing the East as a colonial empire would find its final and most radical application during the Second World War.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".