Coalizing Against Fatmisic and Sanist Targeted Ads of Oppression
Bibliographic record
Abstract
In this article, we do fat studies, mad studies, and critical eating dis/order studies (CEDS) together as methodology. Diversely positioned in relation to gender, sexuality, race, size, disability, and eating dis/orders, we engage in critical conversations across movements, building solidarities in and between our communities to coalize against fatmisia and sanism. We were galvanized to come together in this collective resistance and community building as a subversive response to our shared (research) experiences of being targeted with weight-loss and fat eradication advertisements on social media. These ads communicate that we are medical, moral, and aesthetic problems that need to be intervened upon to become healthy, worthy, and desirable–to qualify as human. Motivated by our commitments to mad-disability justice and fat liberation, we contribute the concept of targeted ads of oppression (TAO) to name the violence that is hurting us. Utilizing affect theory with attention to how our feelings matter, we define TAO and identify four typologies. Our analysis engages with the concept of “recipes” to illustrate how TAO arise from a complex mixing of five dimensions that not only facilitate the existence of TAO, but also promote the propagation of fatmisia and sanism. We conclude by presenting five strategies for mobilizing against fatmisia and offer a theoretically-informed approach for navigating the harmful and affective qualities of TAO in both online and offline settings, which may prove useful to both fat and non-fat scholars and activists interested in anti-sizeist work.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".