The Radicalization of Inner Colonization
Bibliographic record
Abstract
Early on in the First World War Sering was preparing for Germany to be starved by a British Blockade. Erich Keup was an early key contributor to Sering’s thinking about Eastern Europe during the war. Immanuel Geiss and the Border Strip story, the Wartheland, food security, blockades, submarines, and Tirpitz are all discussed, along with the slaughter of the pigs. The inner colonial thinkers suddenly saw Germany as full and turned their sights to the newly conquered East of 1915. Sering’s journey through Poland and Latvia in 1915 was followed by plans for the settlement of two million Germans in Latvia and Courland. Sering then journeyed east in 1916. The Kingdom of Poland, German freedom, Adolf Harnack, Friedrich Meinecke, Ernst Troeltsch, and Otto Hintze are all covered here. Sering discussed the colonial potential of Belarus in the 1917 edited volume Western Russia and its Importance in the Development of Central Europe. Anti-semitism is discussed, along with Schwerin and Lindequist in the East. Schwerin very close to Ludendorff. It then covers Ober Ost, War Land on the Eastern Front , Liulevicius, Brest-Litovsk, a massive German colonial empire in the Eastin 1918, and Sering’s visit to Kiev. Land and people became race and space. The period ended in defeat.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.047 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".