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Record W2588434271 · doi:10.1080/09574042.2016.1256558

Double Melancholy: The (Class) Politics of Loss in Jeanette Winterson’s <i>Why Be Happy When You Could Be Normal?</i>

2016· article· en· W2588434271 on OpenAlexfundno aff
Emma McKenna

Bibliographic record

VenueWomen a Cultural Review · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSubjectivityMemoirPoliticsQueerAmbivalenceNarrativeAestheticsSociologySubject (documents)PerversionUncannyGender studiesSensibilityPsychoanalysisLiteratureArtPsychologyPhilosophyLawEpistemologyPolitical science

Abstract

fetched live from OpenAlex

In his essay ‘The “Uncanny”’, Sigmund Freud claims that ‘the double was originally an insurance against the extinction of the self’. The author suggests that literary writing, particularly memoir, can perform a kind of doubling, enacting this ‘self-preservation’ through ‘self-observation’. In her memoir Why Be Happy When You Can Be Normal?, Jeannette Winterson seeks to convey a ‘doubleness at the heart of things’. The author argues that this ‘doubleness’ functions on two levels, both of narrative and of politics—Winterson’s preoccupation with her subjectivity is informed by politics and her politics are structured around her subjectivity. In order to think through the text’s focus on what the author deems maternal melancholy and ambivalence, the author considers how political melancholy works through and against Winterson’s desires for self-creation. Attending to the themes of writing, loss, adoption and depression throughout, the author sustains a class analysis that is motivated by a queer feminist approach. The author argues that the text works to recall the poor/working-class body into the narrative of the bourgeois subject in order to legitimate the present self—the double—both as exceptional and as different from the other.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.807

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.057
GPT teacher head0.329
Teacher spread0.272 · 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
Published2016
Admission routes1
Has abstractyes

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