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Record W4401441485 · doi:10.1093/hgs/dcae039

<i>European Mennonites and the Holocaust</i>. Mark Jantzen and John D Thiesen

2024· article· en· W4401441485 on OpenAlexaboutno aff
Brandon Bloch

Bibliographic record

VenueHolocaust and Genocide Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCommunism, Protests, Social Movements
Canadian institutionsnot available
Fundersnot available
KeywordsThe HolocaustSociologyEthnologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

In March 2018, a conference on “Mennonites and the Holocaust” took place at Bethel College in North Newton, Kansas, aiming to critically reorient a field often shrouded by mythmaking. The resulting volume, edited by Mark Jantzen and John D. Thiesen, makes groundbreaking contributions not only to Mennonite Studies and European religious history but to Holocaust Studies more broadly. While scholars including Thiesen, Marlene Epp, Benjamin Goossen, and James Urry have challenged postwar mythologies of Mennonite innocence, the present volume updates and expands the field in several ways. First, the essays embrace cutting-edge methodologies in Holocaust studies by placing Mennonites, Jews, German occupiers, and surrounding communities within a common frame. Second, the multiauthor format allows the volume to traverse Germany, the Netherlands, Ukraine, Poland, the United States, Canada, and Paraguay, mirroring the transnationality of the twentieth-century Mennonite experience. Third, the authors engage the full range of roles played by Mennonites in the Holocaust, including as perpetrators, collaborators, enablers, witnesses, bystanders, and rescuers—though with less emphasis on the latter category, in self-conscious distancing from older scholarship. Finally, the book is distinguished by its diverse approaches, from the history of theology and religious institutions to the microhistory of genocide at the local level.

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)0.002
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: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.031
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0020.002
Scholarly communication0.0040.004
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0310.004

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 head0.041
GPT teacher head0.350
Teacher spread0.309 · 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
GenreReview

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
Published2024
Admission routes1
Has abstractyes

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