The Merchant of Venice in Auschwitz: Taking Apart Shylock Using the SCM and BIAS Map
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".