Bibliographic record
Abstract
Knowledge of the dark history and inherent ethical dilemmas of Pernkopf's atlas is essential to individual decisions on use. Seventy-five years after the Holocaust, the legacy of Pernkopf's Atlas of Topographical and Applied Human Anatomy continues to unfold. Informed use of the atlas needs to be integrated in academia and in practice. This paper advocates for the adoption of The Vienna Protocol and improving informed use of the atlas by: (1) updating and inserting an information letter in as many volumes as possible, so that the history can be known before use; (2) conducting and publishing a research study within the medical art community, to examine knowledge of the history of the atlas and elevate awareness; and (3) creating a museum archive and permanent exhibition of the original anatomical illustrations, to document historical facts, disseminate visual evidence, and illuminate embedded controversies. Moving towards informed use, in these ways, provides opportunities for continued ethical discourse, personal reflection and future Holocaust education. Through informed use we memorialize and pay tribute to the Nazi victims portrayed in the atlas.
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 | 0.120 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".