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3. Tripartite Nightmares and Feminist Dreams

2025· article· en· W4412990787 on OpenAlexaff
Jacqueline Cardoso, Amaya Kodituwakku

Bibliographic record

Venue(Un)Disturbed A Journal of Feminist Voices · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicTravel Writing and Literature
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPsychoanalysisPsychologySociology

Abstract

fetched live from OpenAlex

With the proliferation of what we call “cultural noise” in the age of the internet, it can be daunting for feminists (and, indeed, anyone) to avoid the persistent pull of neoliberal individualism. The capitalist, white supremacist, patriarchal tripartite state (bell hooks 2018) strips us of our power by attributing negative moral value to those that they deem a threat to systems of oppression; they label us “the grotesque”. Given this background, this paper asks: How can we begin to deconstruct these social norms without first learning to identify how the grotesque operates? Like Mikhail Bakhtin argues, the grotesque is not solely located in the body, but rather, defined as anything that has been deemed “gross” by social powers. In this paper, we plot out potential strategies for feminist reclamation of the grotesque, pulling on the works of adrienne maree brown, bell hooks, Sara Ahmed, and more. Through a dialogic intersectional approach, we explore a variety of topics: drag, filmmaking, butch/femme lesbian dynamics, the queer and disabled villain, stand-up comedy, physical and digital protest, and religion. This paper aims to explore how feminists have reclaimed the grotesque and what needs to continue being done in the future. Alongside the paper, there is also a digitized version of a zine, which works to represent this discussion through material creation. We ultimately argue that to imagine a feminist future involves turning the nightmares of the tripartite state and turning them into dreams of reclamation and redefinition.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.892
Threshold uncertainty score0.911

Codex and Gemma teacher scores by category

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

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 head0.010
GPT teacher head0.234
Teacher spread0.224 · 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
Published2025
Admission routes1
Has abstractyes

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