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Record W2942590929 · doi:10.33697/ajur.2016.014

Feeding Anti-Semitism: Representations of Jewish Food Practices in Der ewige Jude

2016· article· en· W2942590929 on OpenAlexaff
Forrest Picher

Bibliographic record

VenueAmerican Journal of Undergraduate Research · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean history and politics
Canadian institutionsMcGill University
Fundersnot available
KeywordsJudaismJewish identityArgument (complex analysis)SociologyGenocideRepresentation (politics)Meaning (existential)Jewish American literatureScholarshipContext (archaeology)HumanityHaskalahAestheticsHistoryJewish studiesArtLawPolitical sciencePhilosophyEpistemology

Abstract

fetched live from OpenAlex

The film Der ewige Jude has received a considerable amount of scholarship, but never solely in the context of its representations of Jewish food practices. This paper addresses this void arguing that the representations are used to de-civilize and dehumanize the Jewish people in the minds of the viewers. The representation of Jewish home life around the dinner table is done in such a way as to emphasize filth and bugs. Similarly the conditions of street food are naturalized and used as “evidence” that the Jewish people are a lower race. Later, as the film describes Jewish cultural practices it completely subverts the meaning of Purim in such a way as to access the longstanding prejudices of Jews as “bloodsuckers”. Finally, kosher slaughter is used to separate Jewish people from both a sense of Germanness and a European identity altogether. In fact, I argue this representation served as an argument to deny European Jews their humanity altogether. In all of these ways the film creates a cinematic argument that attempts to justify what would become the mass-murder and genocide of the European Jews. KEYWORDS: Food; Film; Representations; Ghetto; Purim; Kosher

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0050.008
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.002
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 head0.141
GPT teacher head0.446
Teacher spread0.305 · 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 designQualitative
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
Published2016
Admission routes1
Has abstractyes

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