From New York to Moscow and Kyiv: The Wartime Social Life of the Death Toll for Babyn Yar
Bibliographic record
Abstract
The article makes the case for writing the history of the social life of death tolls from genocide. If we trace which death tolls circulated, and how, and what those calculations or estimates meant to the people expressing, transmitting, or receiving them, past information transfers are revealed, as are today’s gaps in documentation. The approach can also help to assess how much contemporaries were interested, during and after genocide. The article writes some pages of the earliest history of the social life of the death toll for Babyn Yar in Kyiv. Six weeks after the massacre of late September 1941, two New York-based news agencies told their subscribers that 52,000 Jews had been killed. Newspapers in the US, Canada, and the UK ignored the figure, until the Soviet media mentioned it. For almost five months, the latter and even the Soviet Commissariat of Foreign Affairs used this number in speaking of the massacre. All this contrasted with those internal Soviet reports that are available for this period, before the Red Army’s recapture of Kyiv in early November 1943. One report in December 1941 approximated the official SS figure for the main massacre, of 33,771 – it spoke of 30,000 Jews. Otherwise, internal official Soviet estimates were higher. Eventually, the very high Soviet number 100,000 made its debut, in a report by the Communist Party. In all, in the unoccupied Soviet hinterland, there was a numerical disparity between published figures and internal estimates. Once back in Kyiv, the NKVD, proclaiming guesswork as a fact, quickly promoted the notion that 100,000 people had been murdered at Babyn Yar, and this became the official minimum in early 1944.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".