Israel Charny’s Attack on the Journal of Genocide Research and its Authors: A Response
Bibliographic record
Abstract
Israel Charny has published an article, “Holocaust Minimization, Anti-Israel Themes, and Antisemitism: Bias at the Journal of Genocide Research” (JGR) in the Journal for the Study of Antisemitism. His specific allegations are bundled together in a single sentence: “minimization of the Holocaust, delegitimization of the State of Israel, and repeat[ing] common themes of contemporary antisemitism”. We write as the authors of articles and contributors to the JGR attacked by Charny. His allegations are false and we reject them. This article shows how they are based on distortions, misquotations, and falsifications of our work.
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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 | 0.036 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.022 | 0.031 |
| Scholarly communication | 0.024 | 0.016 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.069 | 0.080 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".