Religion and Medicine, Again: JHMAS Commentary on "The Lourdes Medical Cures Revisited"
Bibliographic record
Abstract
With their 2012 essay on cures at Lourdes, Bernard François, Esther Sternberg, and Elizabeth Fee performed several good deeds for medical historians. First, they concisely summarize the complex story of organizational change at Lourdes, the most famous healing shrine in Europe. Second, they analyze all the recoveries that were officially certified as remarkable, focusing on the miraculés, their diagnoses, and duration of follow-up. Third, they draw attention to the oft-neglected realm of religious experience within modern medical practice. In a manner that will satisfy physician readers, their approach is unapologetically presentist, relying on retrospective (even revisionist) diagnosis, scrutinizing “medical credibility,” and criticizing documentation to “determine whether the Lourdes ‘cures’ really were cures.” They write of the greater “subjectivity that still pervaded medical decisions” in the past, of medicine's increasing “efficiency,” and of “so-called anemias.” They go so far as to hypothesize a future, reductionist explanation for the scientifically inexplicable in neuro-immunology, the research field of Sternberg.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.041 | 0.055 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".