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Record W4256754801 · doi:10.1353/mdr.0.0045

"How Will They Ever Heal . . .?" Bearing Witness to Abuse and the Importance of Female Community in Sarah Daniels's Beside Herself, Head-Rot Holiday , and The Madness of Esme and Shaz

2008· article· en· W4256754801 on OpenAlexvenueno aff
Heather Debling

Bibliographic record

VenueModern Drama · 2008
Typearticle
Languageen
FieldArts and Humanities
TopicContemporary Literature and Criticism
Canadian institutionsnot available
Fundersnot available
KeywordsWitnessHead (geology)CriminologyHistoryPsychoanalysisArtPsychologyPolitical scienceLawGeology

Abstract

fetched live from OpenAlex

In her three women-and-madness plays, Beside Herself, Head-Rot Holiday , and The Madness of Esme and Shaz , Sarah Daniels presents conflicting views of female madness. While she is critical of a society that labels women's anger or refusal to conform as madness, she goes beyond the simplistic view of women's madness as misogyny to show the severe psychological pain suffered by women who have been the victims of verbal, physical, and particularly, sexual abuse. Healing from this type of abuse, Daniels suggests, is only possible through homosocial bonds with other women. Moments where women remain silenced and even complicit in the patriarchal systems and cycles of abuse that oppress, harm, and stifle all women are contrasted in these plays with those moments in which there is potential for change through speech and through women's support of one another, and it is through this contrast, through both the presence and the absence or subversion of testimony and community in these plays, that Daniels appeals to her audience and stresses that the only way forward for women who have endured such trauma is the establishment of an attentive and supportive female community.

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)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.015
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0150.037
Scholarly communication0.0070.005
Open science0.0010.004
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.038
GPT teacher head0.238
Teacher spread0.200 · 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 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
Published2008
Admission routes1
Has abstractyes

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