Bibliographic record
Abstract
This article sheds new light on illegitimacy in eighteenth-century Britain through an analysis of evidence from 36 parishes across the former Welsh counties Montgomeryshire and Radnorshire. Quantitative analysis of illegitimacy ratios demonstrates that levels were significantly higher in certain, but not all, parts of Wales in the eighteenth century. This evidence is considered in relation to explanatory frameworks used in the analysis of English data, which attempt to account for rising levels through cultural changes that influenced premarital sexual behaviour, and economic opportunities created by industrialization. Welsh evidence appears to present a challenge to these understandings in two key ways: Wales was linguistically different and lacked certain cultural markers which some historians have associated with an eighteenth-century 'sexual revolution', and because the highest levels of illegitimacy were found in agricultural regions of Wales which experienced little or no industrial change. It is argued that Welsh illegitimacy was influenced by a combination of courtship-led marriage customs, a decline in traditional forms of social control and worsening economic circumstances which, on closer examination, appear remarkably similar to London. This analysis provides further evidence that illegitimacy in eighteenth-century Britain was a deeply complex phenomenon governed by diverse regionally specific social and economic influences.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".