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Record W1565349859 · doi:10.3138/flor.28.007

Impatient Griseldas: Women and the Perpetration of Violence in Sixteenth-Century Glasgow

2011· article· en· W1565349859 on OpenAlexaffvenue
Elizabeth Ewan

Bibliographic record

VenueFlorilegium · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies on Reproduction, Gender, Health, and Societal Changes
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsAssertivenessPsychologyCriminologyDomestic violenceVerbal abuseGender studiesSuicide preventionPoison controlHistorySociologySocial psychologyMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.772
Threshold uncertainty score0.280

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.214
Teacher spread0.175 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2011
Admission routes2
Has abstractyes

Explore more

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