Jewish Scientists in Los Alamos during the Manhattan Project: Informing their Perspective and Work
Bibliographic record
Abstract
Since the radio broadcasted that Hiroshima and Nagasaki had been bombed, Americans have had difficulty understanding the science and figures behind the literally earth-shattering invention. Despite the innumerable books, shows, movies, poetry, and more regarding World War II and the atomic bomb, very few portray the role played by Jewish scientists in the American effort. Even before 1945, the Manhattan Project and its contributors have had to strike a delicate balance between confidentiality and transparency towards the public. Secret cities involved in the Manhattan Project such as Los Alamos were met with skepticism and concern, especially during the Red Scare of the 1950s and the director J. Robert Oppenheimer’s trial. Such concerns made it difficult to highlight participants in Los Alamos who could be deemed troublesome or anti-American, including Jews and immigrants. It is important to note that Jews in this case refers to those whom the Nazis and others would have persecuted because of their perceived Jewishness. They themselves may not have considered themselves Jewish, especially those religious non-practitioners or those with half or quarter Jewish blood, but such individual circumstances mattered very little in the wide scale hatred of Jews. To some extent, such prejudice has continued today. Modern textbooks specifically highlight the righteous American men who worked on the project. They are seen as the saviors of WWII who worked tirelessly to stop their German and then Japanese enemies. While such a story is accurate to a point, it devalues the contributions made by the typically overlooked margins of society such as women and minorities. Jews, both foreign and American-born, were an especially important part of the atomic race in Los Alamos. Their personal desire to resist the devastation brought about by Hitler and the Third Reich catalyzed and revitalized the Manhattan Project in Los Alamos.
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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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.055 | 0.014 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".