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Record W4411948309 · doi:10.3138/flor-2023-0003

Rereading Medieval Cases of <i>Raptus:</i> Group Rape and Homosociality in England

2025· article· en· W4411948309 on OpenAlexaffvenueabout

Bibliographic record

VenueFlorilegium · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical Legal Studies and Society
Canadian institutionsAcadia University
Fundersnot available
KeywordsGroup (periodic table)ArtGender studiesHistorySociologyPhysics

Abstract

fetched live from OpenAlex

This article explores the long history of medieval group rapes as performative masculinity and as an avenue to publicly display one’s manhood. By studying medieval England’s eyre court cases, it shows how raptus scholars have previously misinterpreted the potential collective experience of rape in the medieval past. This research demonstrates that rape cases in which there is a single perpetrator accused of rape and other individuals accused of aiding in the rape, have frequently been inaccurately read by scholars as lone rape cases. This article presents alternative readings to medieval cases of raptus and ultimately argues that we must reread rape cases in which multiple men are accused, as consisting of a group rape. Incorrectly reading group rapes as individual rapes works to falsely position group rape as a rare occurrence when, in fact, the eyre court and king’s bench archives suggest otherwise. Exploring what medieval scholars can learn from the Hockey Canada allegations about the prevalence of group rape, we can begin to consider the homosocial bonds it may establish between young men. Connections between elite-level hockey players and medieval knights are drawn to demonstrate the collective experiences that group rapes and initiation rituals may have as a team-bonding activity. Drawing on homosociality theory, this research presents new ideas in conceptualizing how group sexual violence could be used as a medieval masculine community-building exercise and how it resonates with present-day issues.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.785
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.018
GPT teacher head0.295
Teacher spread0.277 · 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 designNot applicable
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
Published2025
Admission routes3
Has abstractyes

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