Policing violence: royal and community perspectives in medieval France
Bibliographic record
Abstract
Violence is, and was, a destructive interpersonal act that occurs both on the large scale through wars, and small scale between two or several people. In medieval France, under the right circumstances, violence was simultaneously policed, and used to police society, especially at the interpersonal level. Men, women, the young, and old were all victims and perpetrators of violence. However, gender and age were significant factors in the legitimization of violence. Men would engage in interpersonal disputes in self-defense, to maintain their honour and reputation, as well as to maintain social order. Women were more likely to be the victims of sexual assault perpetrated by men, but the severity of their attacks was dependent on their age and sexual maturity. These distinctions illustrate that there were some women who were more valued in society than others, for example virgins were pure and had value for marriages. It is the purpose of this thesis to demonstrate that there were legitimate and acceptable forms of violence that could be used to police society. While murder/homicide and sexual violence were deemed to be capital offences, among local communities, where dominant cultural norms superseded “the law”, violence was sometimes considered a productive social force. It could be used to reinforce social values and maintain power structures, especially patriarchy.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.027 | 0.027 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".