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Record W2568969349

Analyzing Ann Quin’s and Kate Millett’s Forgotten Works Through a Mad Reading Practice and Feminist Literary Criticism

2016· dissertation· en· W2568969349 on OpenAlexfundno aff
Sarah Harrison

Bibliographic record

VenueMacSphere (McMaster University) · 2016
Typedissertation
Languageen
FieldArts and Humanities
TopicShort Stories in Global Literature
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaMcMaster University
KeywordsCriticismReading (process)LiteratureLiterary criticismPhilosophyArtLinguistics
DOInot available

Abstract

fetched live from OpenAlex

In my thesis, I engage with recent scholarship in Mad Studies directed towards introducing a Mad reading practice or Mad theory to the discipline of English and academia more broadly. I utilize Mad theory and feminist literary criticism in order to frame my analysis of two forgotten queer Madwomen—British author Ann Quin (1936-1973) and American author, artist, and activist Kate Millett (1934-Present). I consider how Quin’s novel Three (1966) and Millett’s autobiography Flying (1974), as experimental texts exploring bisexuality and polyamory que(e)ry heteronormative monogamy and patriarchal literary convention. I also posit that Quin’s “The Unmapped Country” (1973) and Millett’s The Loony-Bin Trip (1990) deconstruct a perceived tension in feminist literary criticism surrounding whether the figure of the Madwoman is a subversive or silenced figure. In using a Mad reading practice, my analysis focuses on the intersections of sanism with other forces of oppression, as well as how sanist epistemic violence dissuades critically analyzing Mad individuals’ creative or personal narratives as theoretical and political texts. Moreover, I gesture towards the overlooked social exclusions produced by sanist epistemic violence, such as forced institutionalization, unemployment, criminalisation, and homelessness, which suggests the ethical importance of incorporating Mad theory into everyday practice.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.931
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.000

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 head0.011
GPT teacher head0.227
Teacher spread0.216 · 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.

Study designNot applicable
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

Citations3
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueMacSphere (McMaster University)Same topicShort Stories in Global LiteratureFrench-language works237,207