Jewish Population Passport Issue Protocols as a Statistical Source for the History of the Brest Ghetto
Bibliographic record
Abstract
The article describes the meaning of the use of mass sources of the Nazi occupation period to obtain data on the socio-demographic structure of the Jewish population of the city of Brest on the eve of the Great Patriotic War and in the times of the Brest Ghetto. Such a source of statistical data on the history of the Holocaust is fairly rare, since the Ghetto documentation has not survived in the archives of the former Soviet republics. The article focuses on passport issue protocols for the Jewish population dating from the end of the 1941. Quantitative methods used to process the information contained in the documents, taken on the basis of random sampling, enable the author to obtain representative results on the occupational structure of the Jewish population, its gender and age composition, the correlation between the skilled and unskilled workers among the Jews of Brest, the cultural and linguistic status of the Jewish population, as well as the settlement on the territory of the city. The materials of the article are the result of the analysis of 12,000 passport issue protocols and 5,000 entries on the children of the Ghetto. The passport issue protocols for the dwellers of the Brest Ghetto add to the materials available on the Jewish demography of Western Belarus of the first quarter of the 20 th century.
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 | 0.022 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".