Analyzing Ann Quin’s and Kate Millett’s Forgotten Works Through a Mad Reading Practice and Feminist Literary Criticism
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.046 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".