Bibliographic record
Abstract
‘Medicine began at home’, aptly wrote historian Keith Thomas, 1 and ‘Lay medical practice was centred on the family.’ Indeed, self-medication and healing were commonplace in the Early Modern period: ‘Patients often treated themselves, and the women members of the family especially were the sources of medical knowledge and treatment.’ 2 One could add to these statements that medicine was practised by mothers and handed down through the ages by their mothers, grandmothers and great-grandmothers. 3 The influential and innovative sixteenth-century French agronomist Olivier de Serres, Seigneur du Pradel, 4 believed that women were better suited for family or domestic medicine than men because they were naturally ‘officious and charitable’. ‘Mothers’, he wrote, ‘opened their hearts to understanding apothecary, healing minor cuts, wounds, and many other ailments.’ From books, ‘they take several salutory remedies which come to them easily. They also used many remedies they have learned from their experiences.’ 5 Likewise, Juan Vives, the Spanish humanist, instructed wives to look after their husbands when they were unwell. Wives should, ‘treat his wounds, cover his limbs to keep them from the cold’. Wives ‘should look after their husbands themselves, rather than employing their domestics’. 6 These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.006 | 0.019 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".