Talking Past Each Other: Language and Post-World War II Killings in Slovenia
Bibliographic record
Abstract
In the initial weeks after the end of World War II, Josip Broz Tito’s new communist Yugoslav regime summarily executed perhaps 70,000–100,000 (the figures are estimates and disputed) Yugoslav Axis collaborators and opponents of the regime. Public discussion of the killings was taboo in communist Yugoslavia for over three decades. Only in the 1980s, with the loosening of the state’s monopoly on the narrative of World War II, were the killings tentatively and reluctantly acknowledged by the regime in Slovenia. Yugoslavia’s disintegration and the independence of its constituent republics accelerated this process, and gave way to fierce public debate and polemic, which shows little sign of waning, over the narrative and memory of Slovenia’s World War II experience and the postwar killings. Yet missing has been an analysis of the limitations of the language employed in describing and debating the postwar killings, where the use of a single word can betray the assumed ideological convictions of its speaker. “Talking Past Each Other” first offers a historical survey of how the postwar killings have been spoken of (or not) since 1945, navigating the highly divisive contemporary memory politics and memorial landscape in Slovenia. It then examines the ongoing language war over the postwar killings by analysing how suitable some of the more common terms are in describing the killings, including “massacre,” “terror,” “revolutionary violence,” “vengeance/settling of accounts,” “war crimes,” “crimes against humanity,” and “genocide.” It finally offers some tentative suggestions for less controversial terminology that may form the basis for a more historically reflective and less divisive discussion.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".