Book Review: An Unfortunate Coincidence: Jews, Jewishness, and English Law, by Didi Herman
Bibliographic record
Abstract
ARE JEWS TREATED FAIRLY in today's courts in the West?Most of us would answer in the affi rmative without a second thought.But a new, well-documented, and provocative book by Didi Herman, Professor of Law and Social Change at the University of Kent in England, An Unfortunate Coincidence: Jews, Jewishness, and English Law, raises some serious doubts about the way that Jews have been and continue to be treated in English courts.Herman analyzes dozens of cases involving disparate areas of law from the twentieth century and one famous case from the twenty-fi rst.She argues that judges often have shown a shocking lack of sympathy when faced with discrimination against Jews.Th e book takes its title from a 1998 case 3 where a Jew and a non-Jew were on trial for handling stolen goods.4 Th e prosecution claimed in its summary that the Jewish defendant was the most self-regarding, utterly cynical, greedy man, you can't believe a word he says… .A master of deceit… .I draw an analogy with Oliver Twist who is seen in the musical where Fagin … goes through all the money and the lolly and the jewels … because like Fagin he is keeping his hands on his own material … he is very similar… .5
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.012 |
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".