Bibliographic record
Abstract
This paper engages in a new materialist analysis of arts-informed research to demonstrate that fatness and transness materialize in an intra-active relationship. Baradian intra-action explains how each attribute of embodiment carries agential force to create and shape the other. The data analyzed in this paper comes from a research project wherein sixteen people participated in semi-structured interviews to share how they navigate body policing in relation to their non-normative gender identity and body weight/shape/size. Among this group, ten participants worked with artists to generate digital stories, or short videos that pair autobiographical script with curated visuals. The project’s methodology fuses artistic expression with qualitative approaches to explore and express embodied difference, in order to disrupt dominant narratives about under-served communities. A range of themes emerged from participant interviews and art that illustrate the agential properties of the trans/fat intersection. First, participants shared when fat felt like or was framed as a hindrance to their gender transition. This happens because, as many participants reflected, thinness codes culturally valued gender presentations. Second, then, participants considered how they reconstitute fat in their enactments of gender, some using fat to signify masculine strength, others feminine softness, others still playing with the non-binary in-between. Finally, participants described what they do with their bodies, and how their acts remake the meanings of fat and gender.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".