Professional Ethics, Medical Experts and the Famine of 1932-1933 in Soviet Ukraine
Bibliographic record
Abstract
The article studies the state-induced famine of 1932-1933 in Soviet Ukraine as a public health crisis and explores the interplay between medical ethics and medical practices. As state employees and agents of the state, medical professionals participated in organization of the healthcare system and construction of a new, Soviet society. Among other spheres, the revolutionary change concerned medical ethics. Officially, pre-Soviet principles of professional ethics were rejected, and new ethical concepts were determined by class interest and class consciousness. The rapid industrialization, forced collectivization and food requisitions and seizure of the late 1920 and early 1930s resulted in the catastrophic famine and deaths of millions of Soviet citizens. Ukraine was one of the most affected regions, and the explosive spread of epidemic diseases followed mass starvation. In their efforts to cope with this crisis and stop the spread of epidemics from the countryside to urban centres, the authorities imposed disciplinary public health orders that resulted in the intensification of state violence, and hundreds of thousands of rural and urban dwellers were treated by medical professionals. The article examines the role of Soviet medical professionals and their entanglement with ethical discourse and medical practice during the famine of 1932–1933.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".