Impatient Griseldas: Women and the Perpetration of Violence in Sixteenth-Century Glasgow
Bibliographic record
Abstract
In her 1986 overview of medieval women’s lives, Margaret Wade Labarge drew attention to women’s verbal assertiveness. Since then, there have been many innovative studies of medieval and early modern women’s ‘disorderly speech,’ studies which have greatly advanced our understanding of premodern gender relations, dynamics of household and community, and gendered expectations of behaviour. However, women made use of their fists as well as their tongues: insults could all too easily lead to blows. Until recently, less attention has been paid to women’s physical assaults on their opponents, perhaps because the ratio of women to men involved in physical violence has historically been lower than that involved in verbal violence. As Garthine Walker has pointed out, the quantification common in historical studies of crime can result in a tendency to ignore women’s experience: “What tends to happen is that women are counted, and being a minority of offenders, are subsequently discounted as unimportant.” Most studies of violence in medieval and early modern Europe have focused on men’s actions, with women appearing primarily as victims of violence rather than as perpetrators. Moreover, examinations of women and crime have tended to focus on actions which have been characterized by modern historians as particularly ‘feminine’: crimes such as infanticide, scolding, and witchcraft.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".