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Record W4392737564 · doi:10.1017/9781009379816

Staging Disgust

2024· book· en· W4392737564 on OpenAlexaff
Jennifer Panek

Bibliographic record

VenueCambridge University Press eBooks · 2024
Typebook
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsDisgustPsychologySocial psychologyAnger

Abstract

fetched live from OpenAlex

This Element turns to the stage to ask a simple question about gender and affect: what causes the shame of the early modern rape victim? Beneath honour codes and problematic assumptions about consent, the answer lies in affect, disgust. It explores both the textual "performance" of affect, how literary language works to evoke emotions and the ways disgust can work in theatrical performance. Here Shakespeare's poem The Rape of Lucrece is the classic paradigm of sexual pollution and shame, where disgust's irrational logic of contamination leaves the raped wife in a permanent state of uncleanness that spreads from body to soul. Staging Disgust offers alternatives to this depressing trajectory: Middleton's Women Beware Women and Shakespeare's Titus Andronicus perform disgust with a difference, deploying the audience's revulsion to challenge the assumption that a raped woman should “naturally” feel intolerable shame.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.021
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.006
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0210.006

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 head0.029
GPT teacher head0.263
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations8
Published2024
Admission routes1
Has abstractyes

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