Bibliographic record
Abstract
Within ‘obesity epidemic’ narratives, fat is storied as a threat to health, morality, and citizenship at both the population and individual level, and the negative traits that are associated with fatness become attached to marginalized bodies through settler colonial logics that rely on the subjugation of particular bodies (Land, 2018; Strings, 2015; White, 2016). Anti-obesity discourse creates the social conditions whereby fat becomes associated with laziness, incompetence, and lack of self-control (Hartley, 2001; Kargbo, 2013; Throsby, 2007). Embedded in an Anglo-Western, neoliberal context, the Feeling Fat study set out to understand the intergenerational movement and impact of anti-fat narratives that emerged after the 1950s in North America. Drawing on post-humanism and new materialism (Braidotti, 2013; Fox, 2016), I conducted 19 narrative interviews with individuals born between 1955 and 1990, six of whom were mother-daughter dyads. This article reports on findings generated by engaging the post-qualitative approach of “thinking with theory” (Jackson & Mazzei, 2012) as a way of putting participants’ storied accounts directly into conversation with entangled theoretical approaches to engage with both the discursive and affective realities of embodied fat experiences under biopedagogical forces that stipulate how (and how not) to have a body. The findings presented in this article suggest that feminist affect theory (Ahmed, 2004; 2010a; 2010b; 2014) is a powerful tool for discovering fat knowledges that are onto-epistemologically disconnected from ‘obesity epidemic’ narratives and instead centered on the lived materiality of fat life.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".