The Shaping of “Historical Truth”: Construction and Reconstruction of the Memory and Narrative of the Waffen SS “Galicia” Division
Bibliographic record
Abstract
This paper looks at how the memory and, subsequently, narratives of the Waffen SS “Galicia,” later known as the 1st Ukrainian Division of the Ukrainian National Army, are being (re)constructed and presented to a wider audience by scholars, politicians, and World War II veterans. The narratives and political framings of the “Galicia” Division tend to divide into two dichotomous approaches, each presenting itself as “historical truth.” On the one hand, the ex-members are often portrayed as traitors, opportunists, and war criminals. On the other, ex-“Galicians” are seen as those who arguably chose “the lesser of two evils” and joined the German Army in order to defend their motherland against the Soviet invasion and build a nucleus for the Ukrainian army. Rather than follow the well-trodden paths of attempting to justify or condemn the Division’s actions, this paper analyzes how the interpretations of the Division’s identity are presented in contemporary debates, addressing at the same time the concept of memory. It offers a discussion of the political framing of history in contemporary Ukraine and of the challenges that Ukrainian historiography faces with regard to the question of World War II in general and the “Galicia” Division in particular. In this way the paper seeks to contribute to an understanding of the institutionalization of memory and the shaping of national identity through existing and newly emerged narratives about World War II in contemporary Ukraine.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.040 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".