“They didn’t know fat was awesome”: fat activism and fat \ncommunity in Toronto, Canada
Bibliographic record
Abstract
My project focuses on fat activism as a direct challenge to the discourses of the \nso-called “obesity epidemic.” Looking at the embodied experiences of fat activists, \nincluding my own, I look at the multiple ways fat activism is carried out in the Toronto \narea. I situate these experiences of fat activism in two ways. One, I look to examine both \nindividual and group activisms within and against the negative stereotypes of fatness that \ncome from obesity epidemic discourse. Using food, fitness, and fashion, I explore how \nour experiences as activists work to reinscribe fatness in more positive and celebratory \nways. Secondly, I investigate the individualizing discourses of obesity and the loneliness \nand shame these create for fat individuals. As a response to this, I examine fat community \ncreation as a fat activist project. Within this, I explore counter-discourses of fat activism \nand how they work both to support fat people and revalue fatness as an embodied \nexperience. Finally, I look at these counter-discourses to investigate issues of inclusion \nand exclusion within fat community.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.046 | 0.015 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".