Knowledge of the Mass Murder of Jewish People Possessed by Ordinary German
Bibliographic record
Abstract
In 1933, the Nazi Party, led by Adolf Hitler, came to political power in Germany. As a direct result of the Nazi’s actions, approximately 6 million Jewish victims were killed. The Nazi Party members were undoubtedly responsible for these results, but were the non- party Germans? To answer this sensitive question, the extent of knowledge of these events must be investigated. To what extent did “ordinary” German civilians know about the extermination of Jews during the Holocaust in Berlin from 1942 to the end of the Second World War? The population must be categorized by those who knew about the Jewish deportations and murders, those who chose to know, those who chose not to know, and those who did not know. To investigate this idea, oral interviews were collected. They hold value as a first hand perspective, but have limitations of dishonesty and censorship of information. A large collection of survey information from 1985 was heavily considered, in addition to various secondary sources such as articles, videos, and books, and primary sources such as maps and photographs. After weighing probable statistics and popularity of Nazi ideology, evidence supports the idea that more Germans chose not to know about the extermination of Jews than any other extent, due to the high number of Nazi ideology supporters, high degree of terror propaganda, and indoctrinated youth.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".