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Record W2746859212 · doi:10.60770/bk0x-cn20

Policing violence: royal and community perspectives in medieval France

2016· dissertation· en· W2746859212 on OpenAlexaff
Allison Tracy Maria Bailey

Bibliographic record

VenueMount Royal University Institutional Repository (Mount Royal University) · 2016
Typedissertation
Languageen
FieldSocial Sciences
TopicMedieval and Early Modern Justice
Canadian institutionsMount Royal University
Fundersnot available
KeywordsAncient historyCriminologyPolitical scienceHistorySociology

Abstract

fetched live from OpenAlex

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and sexual violence were deemed to be capital offences, among local communities, where dominant cultural norms superseded “the law”, violence was sometimes considered a productive social force.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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.683
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.012
GPT teacher head0.235
Teacher spread0.223 · 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.

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
Published2016
Admission routes1
Has abstractyes

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