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Record W3124495700 · doi:10.3389/fpsyg.2020.602113

The Merchant of Venice in Auschwitz: Taking Apart Shylock Using the SCM and BIAS Map

2021· article· en· W3124495700 on OpenAlexafffund
Susan Knutson

Bibliographic record

VenueFrontiers in Psychology · 2021
Typearticle
Languageen
FieldPsychology
TopicMemory, Trauma, and Commemoration
Canadian institutionsUniversité Sainte-Anne
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsStereotype (UML)ComedyAffect (linguistics)ArtOperaLiteraturePsychologySociologySocial psychologyCommunication

Abstract

fetched live from OpenAlex

In response to Frontiers’ 2020 Call for Papers on “Stereotypes and Intercultural Relations: Interdisciplinary Integration, New Approaches, and New Contexts,” my paper integrates the scientific study of stereotypes with a literary-theatrical exploration of stereotyping. The focus is on Tibor Egervari’s post-Auschwitz adaptation of Shakespeare’s anti-Semitic comedy The Merchant of Venice , with a very brief look at his related work on Christopher Marlowe’s The Jew of Malta and his 1998 collaboration with conductor Georg Tintner on a touring production of composer Viktor Ullmann’s and librettist Peter Kien’s one-act opera, The Emperor of Atlantis, or Death’s Refusal , composed in the “model” concentration camp Terezín (Theresienstadt), in 1943–1944. Egervari’s theater art critically deconstructs what he calls “the Old Jew” stereotype in specific ways highly readable using the Stereotype Content Model (SCM) and Behavior from Intergroup Affect and Stereotypes (BIAS) map. Theater performance can sometimes embody the forceful dynamic traced by the BIAS map, from cognition to affect to behavior. Egervari’s original adapation, which sets The Merchant of Venice in Auschwitz, reveals this dynamic clearly. My interdisciplinary study of Egervari’s theatrical-cultural work validates the SCM and BIAS map for literary studies and interprets the Shylock stereotype in the terms of those models and through the lens of Egervari’s anti-Nazi adaptation of Shakespeare’s Merchant .

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score0.433

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 studies0.0000.000
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 head0.091
GPT teacher head0.369
Teacher spread0.278 · 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 designObservational
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 routes2
Has abstractyes

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