On Being the ‘Fat Person’: Possibilities and Pitfalls for Fat Activist Engagement in Academic Institutions
Bibliographic record
Abstract
This article addresses the possibilities and pitfalls for fat activist engagement in academic institutions through the framework of the ‘fat person.’ Drawing from Emily Henderson’s (2019) ‘gender person’ in academia framework, we connect our own experiences as fat studies scholars, teachers, and activists with the experiences of other scholars in our field to construct a framework of understanding the role of the fat studies expert, or the ‘fat person,’ in the academy. The raw material for this article was written over the course of two extended online chat sessions between the authors, which took place during the summer of 2020. Our conversations were seeded by our prior histories as fat people and fat academics, and by our pre-existing collaborations: as supervisor and graduate student, co-researchers, and through teaching together in a fat studies course. Throughout this article we draw on scholars in our field who have explored their experiences as fat academics, fat researchers, fat students, and fat teachers. We argue that this framework is a useful step in furthering understanding of what it means to be positioned as the ‘fat person’ within an academic institution. We are embedded in the strength of our communal and embodied experiences, and at the same time, we are also aware of the potential ethical challenges of working from a place that is firmly grounded in community knowledge. Our hope is that other scholars, particularly fat studies scholars, will build from the framework we are suggesting here to further understandings of how the ‘fat person’ is constructed—and resisted—within the academy
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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 | 0.061 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.052 | 0.158 |
| Scholarly communication | 0.043 | 0.043 |
| Open science | 0.005 | 0.049 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.008 | 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".