The Prussian Settlement Commission and Its Activities in the Land Market, 1886–1918
Bibliographic record
Abstract
Inner colonization, as is clear from the chapters in this volume, can take many forms. The farther back one goes in history, the more likely it is that the colonization effort was directed at populating empty lands. This was the case, for example, in the efforts of the Habsburgs to attract settlers to the Southeastern areas of their Empire in the eighteenth century, after expulsion of the Turks. Even schemes in modern times, such as Nikita Khrushchev’s virgin lands scheme in the Soviet Union, or the settlement schemes in Sri Lanka in the 1960s, were primarily aimed at bringing new land, or underused land, into cultivation. In the late nineteenth and early twentieth century, however, there existed an official government settlement scheme that was unusual in that it was directly aimed at changing the ethnic balance of population in an already settled region,1 and undertaken primarily for political, rather than economic, ends.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".