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Record W7011413758

A Malleable Identity: The Immigration of Ethnic Germans to North America, 1947-1957

2021· dissertation· en· W7011413758 on OpenAlexaboutno aff

Bibliographic record

VenueOakTrust (Texas A&M University Libraries) · 2021
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationEthnic groupGermanRefugeeWorld War IISettlement (finance)Ethnic historySpanish Civil War
DOInot available

Abstract

fetched live from OpenAlex

This dissertation is a comparative study of ethnic German Mennonites and ethnic Germans of other faiths from Chortitza, their experiences during World War II, and their immigration between 1947 and 1957. This research explains why some World War II refugees immigrated to the United States whereas others went to Canada. Employing the ethnic German settlement of Chortitza in Ukraine as a case study shows that religious and ethnic identity played a determining factor in immigration. In 1942, Chortitza's inhabitants were eighty percent Mennonite and twenty percent Lutheran, Catholic, or Seventh-day Adventist. After arriving in Germany toward the end of World War II as a result of resettlement and flight, those whom the Soviet Union did not repatriate were eager to immigrate to North America, or even South America if all else failed. Ethnic Germans did not qualify for immigration as Displaced Persons. Displaced Persons were citizens of Allied countries or those who had been forcibly removed from their homes during World War II. Therefore, Mennonite ethnic Germans from Chortitza claimed a Dutch rather than ethnic German identity, pointing back to their Anabaptist faith's origins in the sixteenth century. Religiously affiliated U.S. organizations like the Mennonite Central Committee argued for their constituents' eligibility as Displaced Persons by exerting pressure on the International Refugee Organization, lobbying governments, and forming relationships with U.S. State Department and immigration officials. Organizations helped determine the destinations of ethnic German immigrants based on immigration networks and organizations' histories.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score0.472

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.197
Teacher spread0.185 · 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 source (direct Gemma or distilled Codex), 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
Published2021
Admission routes1
Has abstractyes

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