On the 80<sup>th</sup> anniversary of the Racial Laws. Articles reflecting the current scholarship on Italian Fascist anti-Semitism in honour of Michele Sarfatti
Bibliographic record
Abstract
This special issue of the Journal of Modern Italian Studies, edited by Annalisa Capristo and Ernest Ialongo, marks the 80th anniversary of the implementation of the Racial Laws in Fascist Italy. It is an opportunity to assess the evolution of the historical literature on Fascist anti-Semitism and to mark future directions for research, but also to pay homage to Michele Sarfatti, who was critical in the development of the current state of the historiography on the subject. Where the earlier work, before the 1980s, was founded on the idea of ‘Italiani brava gente’, wherein Italy’s role was downplayed in the persecution of the Jews and in the Holocaust, that Italians were simply too humane to have participated in such horrific events, Sarfatti’s work launched a veritable revolution in the field, which dismantled all the tenets of the original consensus. This introduction surveys these developments, and summarizes the contributions of the varied authors published here who continue to challenge old truths and bring us closer to a more full and accurate understanding of Fascist anti-Semitism.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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".