The History of the Germans from Mérk and Vállaj, Deported to the Soviet Union for Forced Labour 1945–1949
Bibliographic record
Abstract
Abstract The deportation - in German: Verschleppung - was a ‘taboo' for a long time. However, the works born since the change of regime provide an excellent and overall picture about this painful historical act. At the same time, it is desirable to get a more precise picture by examining the detailed history of the deportation in the case of the individual settlements. Merk and Valla), the Swabian settlements in the Szatmar region, in the eastern part of the country, lie on the periphery in several aspects. Still, considering the numerical proportion of their population, the most displaced persons were deported by the Soviets, as war criminals, from here in 1945 - a quarter of whom never saw their beloved ones and home country again. It is the particular tragedy of this fact that those deported were at least as much bound to their recipient country, the Hungarian nation, as to their German nationality. They are not criminals of war but victims of the war of racial discrimination. ‘Who will be responsible for these people suffering innocently?’ - puts the question Ferenc Juhasz, parish priest in Merk at that time. Giving an answer is the task of all of us. The paper seeks to explore a segment of the micro-texture of the country-wide, and even wider, regional trauma of this community, based on diary excerpts from the period as well as on individual, specialized literature research.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".