Philosemitism, antisemitism and ‘the Jews’: perspectives from the Middle Ages to the Twentieth Century
Bibliographic record
Abstract
Contents\nPreface; Introduction: The wide field of relations?, Tony Kushner with Nadia Valman. Part I Theorising Tolerance and Intolerance: Intolerance and tolerance: only one 'one and only' god or more', Gavin I. Langmuir; The power of tolerance, David Theo Goldberg; Reading intolerant texts in a tolerant society, Norman Solomon; The limits of tolerance: nation-building and what it means for minority groups, Mark Levene. Part II Philosemitism, Antisemitism and Intolerance: Jonah the Jew: the evolution of a biblical character, Yvonne Sherwood; The Jews and the cross in the middle ages: towards a reappraisal, Elliott Horowitz; Albert the Great on the Talmud and the Jews, Irven M. Resnick; 'Inward' and 'outward' Jews: Margaret Fell, circumcision and women's preaching, Claire Jowitt; Enlightenment and exclusion: Judaism and toleration in Spinoza, Locke and Bayle, Adam Sutcliffe; The limits of toleration in enlightenment Germany: Lessing, Goethe and the Jews, Ritchie Robertson; The slave, the noble and the Jews: reflections on section 7 of Nietzsche's On the Genealogy of Morals, David M. Seymour; Antisemitism in Canada: the legal dimension in context, Thomas S. Kuttner; Offending the memory? The Holocaust and pressure group politics, Tony Kushner. Bibliography; Index.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".