Bibliographic record
Abstract
The 2019 Toronto Symposium, THE VIENNA PROTOCOL: Medicine's Confrontation with Continuing Legacies of its Nazi Past, was sponsored by Biomedical Communications, Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto and the Neuberger Centre for Holocaust Education. https://www.holocaustcentre.com/hew-2019/the-vienna-protocol 
 Prof. Leila Lax, coordinated the Symposium and was inspired by its presenters to create an online collection of Holocaust education resources. She is grateful to the Editor-in-Chief, Gary Schnitz and the Journal of Biocommunication Management Board for their dedication to scholarship, ethics, and the advancement of knowledge, in support of this Special Issue, that deals with contemporary controversies from a dark time in history, that is part of our professional legacy - and memory. This Special Issue is dedicated to the memory of the victims portrayed in the Pernkopf atlas.
 Image credit: Table of Contents image provided by the Medical University of Vienna, MUW-AD-003250-5-ABB-151.
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 | 0.007 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.016 | 0.021 |
| Insufficient payload (model declined to judge) | 0.046 | 0.033 |
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".