Fat fuckers and fat fucking: a feminine ethic of care in sex therapy
Bibliographic record
Abstract
Through the regulation of both femininity and fatness, dominant norms in queer communities construct fatness and femininity as excessive, desexualised/hypersexualised, and undeserving of sexual desire, pleasure, and care. Care, as a feminine ethical stance emphasising relationality and interdependency, is not typically associated with fucking, yet is critical in sex therapeutic work and interventions. In this article, we contend that fat scholarship, femme theory, and care ethics offer productive intersections in terms of crafting an ethic of care in sex therapy practice and activism for fat bodies of all genders. Using the example of the Fat Fuckers workshop developed in Ontario, Canada, offered internationally and online, this article describes how sex therapeutic work that combines fat activism, care, community building and relationality works at the intersections of femme theory, fat studies, and care ethics. This article combines theory with praxis by describing the Fat Fuckers workshop as a form of fat activism that simultaneously promotes fat identification and care for fat bodies in sex therapy while illustrating a nuanced form of feminine relationality for fat subjects and sexualities. Through this, practical tips are described for practitioners (e.g. sex therapists, activists and sex workers) with theoretical implications for sexuality scholars.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.014 | 0.081 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".