Bibliographic record
Abstract
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 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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.053 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".