References to the Holocaust in English Law Reports
Bibliographic record
Abstract
Few of those who were adults in the 1940s imagined they would live to see the attempts made by some contemporary revisionist historians to falsify or even expunge from the record events which so many have known from personal experi? ence. Such historians seem to fall broadly into two types. The more extreme group is exemplified by David Irving, Robert Faurisson, Arthur Butz and Fred Leuchter, the first of whom was fined ?12,000 in January 1993 by a German court for claiming at a meeting that there were no gas chambers at Auschwitz and that the death camp there was built as a fake. They seem to have learned from Hitler the art of the big lie, later exploited and used to devastating effect by his Minister of Propaganda, Josef Goebbels. It was outlined cynically by Hider in Mein Kampf in the following terms: 'The broad mass of a nation will more easily fall victim to a big lie than to a small one.' The second category of revisionist historians has been described by Professor Donald Cameron Watt of the London School of Economics as authors of
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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".