Slipping Through the Gate: Trusting Daubert to Reveal the 'Pseudo-Historian' - Troubling Lessons from Holocaust-Related Trials
Bibliographic record
Abstract
In light of the prominence of expert historians at trial, Part III explores federal courts' gate keeping role under the Supreme Court's Daubert gate keeping test, as applied to historians serving as expert witnesses. In Irving, a libel case brought by Holocaust denier David Irving against Professor Deborah Lipstadt based on statements in her book, Denying the Holocaust, much of the case focused on plaintiff David Irving's flawed historical methodology regarding Auschwitz (Irving believes Nazis did not use gas chambers to murder Jews at Auschwitz). In the Zundel trials (Zundel was tried and then retried on the same charge) the pseudo-historian threat was realized, as the defense called Irving to testify as an expert historian about gas chambers at Auschwitz, here under Canadian law, where admissibility focuses on credibility rather than reliability. In Irving, Judge Gray did not have to gather, analyze, and synthesize countless historical documents (many of which were in German) concerning Auschwitz because Robert Jan van Pelt and the other defense experts did this work for him. Procedures Should Be Altered for Presenting Historian Expert Testimony The following solutions are aimed at allowing a reliable historian expert sufficient latitude in the courtroom to express his opinion in a manner that comports with his craft.
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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 | 0.028 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.035 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.014 | 0.019 |
| 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".