Bibliographic record
Abstract
This paper proposes new materialist theory as a framework for emancipating fat, queer, and traumatized bodies from oppressive significations embedded in written discourse and social narratives that often legitimize and perpetuate systemic inequalities, contributing to sustained marginalization and othering. Through a critical examination of dominant epistemologies, I argue that binary constructions generate conceptual divides such as fat/thin, white/other, gay/straight, male/female, human/non-human and so on that systematically marginalize and exclude bodies that fall outside of these boundaries. To address these exclusions, I propose a twofold approach to inclusion: first, by dismantling hierarchical conceptualizations of matter, and second, by critically rethinking matter, difference, and positionality. Drawing on the work of key theorists, including Karen Barad, Donna Haraway, Alison Kafer, Ramanpreet Annie Bahra, and Sarah Ahmed, among others, this paper blends perspectives from fat, queer, and disability studies to confront oppressive and harmful viewpoints of corporeality. By reframing how we engage with materiality and bodily difference, we can foster more equitable representations and dismantle harmful discursive structures that perpetuate exclusion to reach a more holistic, inclusive viewpoint. Throughout the paper, I will include my own new materialist-inspired paintings that I consider to be in conversation with the text. I encourage you, the reader, to engage with both text and image as you go along.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.059 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".