Medical Science in the Light of a Flawed Study of the Holocaust
Bibliographic record
Abstract
In her PhD thesis Reading Fleck: Questions on Philosophy and Science (Hedfors, 2006) and her papers based on it, Eva Hedfors proposes a sci entifically informed reading of Ludwik Fleck that aims to contest the (puta tive) mythology of Fleck. According to Hedfors, Fleck is believed to have been an important scientist. However, a careful reading of his scientific papers, she claims, reveals that Fleck's studies were poorly done, often meaningless and of doubtful ethical value. Hedfors also hints that one of Fleck's aims in promoting his views on science as a social endeavour was to legitimate his own scientifically weak and ethically suspicious research. Hedfors has also a very low opinion of Fleck's epistemological thought. In the preface to her thesis she explains that when she first read Fleck, she viewed him as a 'Sokal before Sokal on a rather local level', and found a widespread interest in his writings, 'one of those inscrutable facts we often face'.1 This is surely a legitimate point of view. Hedfors' efforts to deflate the Fleck myth can be seen, however, as a somewhat misguided endeavour. Researchers interested in Fleck's life and science provided many years ago a realistic assessment of his scientific achievements. They stressed that in the 1920s and 1930s, when he wrote his important epistemological studies, that Fleck worked in a peripheral 'service' discipline (serology), in a periph eral country (Poland) and a non-academic setting (he headed a routine analysis laboratory). Historians of science and medicine also are aware of the fact that Fleck's scientific papers were as good or as bad as other average studies in his scientific domain.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Science and technology studies Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.066 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".