Bibliographic record
Abstract
This book tells the little-known stories of three all-Jewish battalions formed in the British army as part of the Allies' Middle East campaign, recruiting soldiers from the United States, Canada, England, and Argentina. Many of the soldiers, ranging widely in education level, social class, and combat experience, were displaced immigrants or children of such immigrants. Together, they coalesced into the all-Jewish battalions: "the liberators of the Promised Land." The ranks of the Jewish Legions included some who would become prominent leaders, such as David Ben-Gurion, Israel's first prime minister, and Yitzhak Ben-Zvi, Israel's second president; however, this book focuses on the experiences of ordinary soldiers who served alongside them. Drawing on diaries, memoirs, and letters, the book follows their journey at sea through unrestricted submarine warfare; by trains and trucks through Europe, Egypt, and Palestine; and their battlefield experiences. The authors show how these Yiddish-speaking young men forged a new kind of soldier identity with unique Jewish features, as well as an evolving sense of nationalism.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.050 | 0.032 |
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".