Offensive weapons: gender, violence and metaphor in the records of medieval English common law:North American Conference on British Studies, Montreal, 2025.
Bibliographic record
Abstract
An entry on the King’s Bench plea roll for Easter 1435 tells us about proceedings against a Norfolk clerk, Thomas Hervy of Testerton.[ KB 27/697 Rex m.5. Linked scan from AALT.] Amongst other things, he was alleged to have broken into the house of John Serjeant of Colkirk, on 1st October 1433, and to have wounded Margaret, John’s wife, by stabbing her with a lance or dagger. Medieval legal records are full of accusations of non-fatal injuries of one sort or another, and we know that carrying a knife or dagger (if not a lance) was commonplace; this case, however, is one in which it really is neither a dagger, nor yet a lance, that we should see before us, but a penis. In all likelihood, the real allegation was not one of stabbing, but of a sexual offence. This case, and a small number of others, refer to injuries inflicted with suggestively-named ‘carnal lances’ and/or ‘bollock-hafted daggers’, and may contain further material making it tolerably clear that the context was one of sexual offence. Literary scholars are familiar with the long tradition of imagery centring on fighting and weapons, in connection with sex and with male genitals, but its appearance in legal sources is less well-known. This paper considers what such examples might add to scholarship on rape and sexual offences in medieval law, and offers some thoughts on the gendered use of metaphor in medieval law more generally.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.004 |
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".