Diet, Nutrition, Regimen: Food and Healthcare in 18th-century British Midwifery
Bibliographic record
Abstract
The general aim of this contribution is to offer a historical and diachronic linguistic analysis of a corpus of works on 18th-c. British midwifery in order to examine and highlight the role of food and healthcare for women and children as it emerges from medical writing. The investigation is essentially focussed on (i) the terminology related to diet, nutrition, and regimen, and (ii) the lexical network that emerges around these lexemes, especially in relation to the discourse of ‘food and/in healthcare’ or, in a more general perspective, to the notion of maternal and child wellbeing. The approach is qualitative, with a specific focus on selecting and analysing extracts of different length and complexity from the corpus. However, to retrieve the words diet, nutrition, regimen, collect data, and identify relevant co(n)texts of use (KWIC) and recurring sequences, a preliminary corpus-based quantitative approach is employed. The perspectives in which data, i.e., extracts and examples, are provided and discussed necessarily refer to the social history of medicine (e.g., the function and role of midwifery in the period considered). Results highlight a variety of discourses on and around diet, nutrition, regimen, along with a variety of multiword expressions (e.g., collocations, frequent patterns, clusters) which lexicalise the notions of healthcare and wellbeing as major issues, especially in late 18th-century medicalised midwifery practice.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".