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Record W2586838769 · doi:10.1515/auseur-2015-0006

The History of the Germans from Mérk and Vállaj, Deported to the Soviet Union for Forced Labour 1945–1949

2015· article· en· W2586838769 on OpenAlexaboutno aff
Richárd Tircsi

Bibliographic record

VenueActa Universitatis Sapientiae European and Regional Studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean history and politics
Canadian institutionsnot available
Fundersnot available
KeywordsDeportationGermanHuman settlementTabooRefugeeNationalityTragedy (event)Quarter (Canadian coin)HistoryPopulationWorld War IISpanish Civil WarLawEconomic historyGenealogySociologyPolitical scienceImmigrationDemographySocial science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.071
GPT teacher head0.275
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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