Bibliographic record
Abstract
Chapter 6 examines the period from 1944 to May 1945. How did chaplains deal with impending defeat? The chaos of retreat brought many challenges. Chaplains witnessed the death throes and killing frenzies of Hitler’s Germany, as atrocities continued to the bitter end. Chaplains ministered to the Wehrmacht amid the destruction of the Jews of Hungary, assault on civilians in the name of anti-partisan warfare, and death marches as concentration camps were shut and guards forced prisoners out onto the road. The physical demands of the chaplains’ work increased as their numbers dwindled and those left lacked supplies of all kinds. Newly created NSFOs, Nazi leadership officers, competed with them for soldiers’ hearts and minds. Yet chaplains found they were more valued in times of defeat. At the front they reminded soldiers of their oath of obedience, while they comforted bereaved families at home. Some Greek Orthodox chaplains were allowed to minister to Ukrainian Waffen-SS men in the Galicia Division. Yet even as chaplains continued to serve, they began quietly to disregard the regime’s will when it conflicted with institutional self-interest, e.g. in appointing new base chaplains.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 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".