“Why are you hiding here?”: Counter-Narrating Antisemitic Master-Narratives in Bernard Malamud’s The Fixer
Bibliographic record
Abstract
When Mendel Beilis, a Jew, was accused of having murdered a Christian child in Kyiv in 1911, the allegations drew on centuries-old “blood libel” legends, dating back to the Middle Ages, in which Jews purportedly sacrificed Christian children for ritual purposes. While Beilis eventually was acquitted of the charges, the master-narratives that drove them have proved resistant to counter-narration. Bernard Malamud’s 1966 novel The Fixer, by fictionally attempting to retell Beilis’s story through the character of Yakov Bok, provides a “critical reinterpretation […] of dominant narrative models” (Meretoja 2021)—a powerful counter-narrative, not only to the specific tale of Beilis, but also to the longer-standing claims that continue to buttress antisemitism.
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 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.030 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".