Bodies at the Intersections: Refiguring Intersectionality through Queer Women’s Complex Embodiments
Bibliographic record
Abstract
In this article we examine the challenges and possibilities of mobilizing intersectionality as a theoretical and methodological construct through a collaborative, arts-based research project. This project, Through Thick and Thin, explored how persons in queer communities who identify (partially or wholly) as women and who claim multiple intersecting positions negotiate, are affected by, and resist body ideals and body management expectations. We present and analyze a selection of multimedia stories (videos) that feature assemblages of queer sexuality, gender expression and identity, and other identifications (race, class, indigeneity, ability, age, etc.) in confrontation with body-based stigma, expectations around eating and exercise, and experiences of pathologization. Reviewing relevant debates within the intersectionality literature, we reflect on how our research team enacted intersectionality as an active, integral part of the research process. We suggest that multimedia storytelling enabled video makers to revise and undermine dominant accounts of their complex, unpredictable, and irreducible bodies. Creative accounts of complex embodiment push the corpus of research on distressed eating and fatness to recognize and better account for bodies at the intersections and press the field of intersectionality studies to consider new ways of conceptualizing intersectionality to account for the complexity of embodiment.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.068 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".