Fat is a Social Work Issue: Fat Bodies, Moral Regulation, and the History of Social Work
Bibliographic record
Abstract
Fat bodies are discriminated against in a variety of individual and structural ways. On an individual level, the experiences of fat stigma are debilitating. On a societal level, the “war on obesity” is a focal point for social policy in both Canada and the United States. Social work, as a profession that considers individual experiences and contextualizes these experiences within systems and structures, must thus consider the implications of bodies that are perceived as deviant on the basis of size. Yet there is a dearth of scholarship that positions fat stigma and the size acceptance movement as allied with other realms of activist social work. This article addresses this omission by considering the need for an incorporation of size acceptance and fat activism into social work scholarship and practice. This is accomplished through three main themes. First, I consider the nature of fat oppression and the need for antioppressive social work practitioners and scholars to give credence to the real implications of fat. Second, I examine the rhetoric of the “obesity epidemic” and consider why social workers need to be critical of social policies that stem from this discourse. Finally, I suggest that the tones of social control and moral panic that underpin much of the discourse around fat bodies are reminiscent of other concerning trends within the history of the social work profession.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.185 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".