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Record W2591876784 · doi:10.3138/seminar.53.1.04

Female Madness in the Age of Neo-Liberalism in Charlotte Roche’s Novels <i>Wetlands</i> and <i>Wrecked</i>

2017· article· en· W2591876784 on OpenAlexvenueno aff
Petra Volkhausen

Bibliographic record

VenueSeminar A Journal of Germanic Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsnot available
Fundersnot available
KeywordsAgency (philosophy)LiberalismContext (archaeology)GermanSociologyGender studiesArt historyHistoryLawPolitical scienceSocial sciencePoliticsArchaeology

Abstract

fetched live from OpenAlex

This essay examines female madness in Charlotte Roche’s novels Wetlands and Wrecked as the (failed) attempt to conform to the twenty-first-century image of a successful woman who can have everything if only she tries hard enough. Saturated by neo-liberal maxims of individual agency and self-optimization, the protagonists perceive their madness to be an issue and flaw they have to manage on their own. Through the employment of a “neo-liberal madness,” Roche disconnects women’s psychological issues from the wider context in which they are embedded, failing to criticize the larger societal structures that continue to prevent German women from achieving true equality. Roche’s novels are a recent expression of the feminist literary tradition of critical madness – with the difference that Roche’s protagonists are madwomen not in the eye of society but in their own understanding.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.009
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.015
Scholarly communication0.0080.004
Open science0.0010.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.090
GPT teacher head0.384
Teacher spread0.293 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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