Mineral Waters, Electricity, and Hemlock: Devising Therapeutics for Children in Eighteenth-Century Institutions
Bibliographic record
Abstract
The development of paediatric medicine as a formal field of medical specialisation is usually traced to the mid-nineteenth century at the earliest. While it is true that formal specialisation in children's medicine was not, on the whole, typical for eighteenth-century medical practitioners, many displayed a deep and lasting interest in the diseases of children, and were consequently eager to develop therapeutic practices which could be targeted at infants and children. This led to a variety of attempts at innovation, many of which benefitted from the co-operation of, and opportunities afforded by, institutions. By examining the efforts of several medical practitioners at the London Foundling Hospital and at the Dispensary for the Infant Poor, this article explores how eighteenth-century medical practitioners used their affiliations with institutions to address the problems of devising or adapting therapeutic practices and treatments for children. In tailoring medical practice to suit children and, more specifically, in using institutions to do so, medical practitioners were demonstrating that child patients required special consideration, that children's diseases could be managed medically and with the benefit of new approaches and methods, and that children's health, as a whole, was the province of medical practitioners.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.036 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".