Holocaust, Genocide, and the Law: A Quest for Justice in a Post-Holocaust World by Michael J. Bazyler
Bibliographic record
Abstract
LAW IS COMMONLY THOUGHT OF as an antidote to genocide rather than its facilitator. In Holocaust, Genocide, and the Law, Professor Michael Bazyler of Chapman University’s Fowler School of Law refutes the notion that the Holocaust was an extralegal event—instead, he isolates the law as the preferred instrument of wholesale murder and destruction. The book traces the long shadow that the Holocaust has cast on the contemporary corpus of international law and many legal systems across the world. While it tells the unfolding catastrophe of the Holocaust as a legal history, the book considers the legal triumphs that followed the catastrophe in their entire context. Specifically, the book explores the legal means that have been used in the last seventy years to redress historical wrongs, obtain justice for victims, and prevent future genocides. These legal means, which Bazyler labels as “Post-Holocaust law,” are shown to have developed in an organized fashion over time to become a discrete body of law. Between masterfully balancing the law’s ability to ruin with its capacity to redress, Bazyler clearly asserts one point: Post-Holocaust law does not yet fit the Post-Holocaust world.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".