“They Perished in the Cause of Science”: Justus von Liebig’s Food for Infants
Bibliographic record
Abstract
In 1867, controversy erupted when Jean-Anne-Henri Depaul, a Paris accoucheur, tested Justus von Liebig's new "food for infants" on four newborns, all of whom died within days. This paper examines the origins of Liebig's food, the debates in the French Academy of Medicine after Depaul's experiment, and how the events were discussed in the medical and popular presses. I argue that the controversy was shaped by a number of interconnected concerns, including the product's impracticality, disagreements within the field of chemistry, the riskiness of Depaul's experimentation, Liebig's problematic celebrity, the potential hubris of trying to emulate a natural product, and national tensions between France and Germany. Infant feeding was an emotionally charged and highly politicized site where multiple interests, anxieties, and ways of knowing collided. Although commercial infant foods, many of which made reference to Liebig in their advertising, would ultimately find popularity in the last decades of the nineteenth century, close attention to the first years of Liebig's product demonstrates that its credibility as a "scientific" mode of infant feeding was far from assured. Rather, Liebig's milk illustrates the early challenges of constructing and enforcing knowledge and trust at the intersection between food, science, and infant life, in both professional and popular arenas.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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, 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".