Fatphobia as Marginalization: The Impacts on Women in the Public Sphere
Bibliographic record
Abstract
This paper seeks to explore the impact that fatphobia has on Western society, specifically the female body. Using existing literature, this research aims to deepen the knowledge and experience around fatphobia and its pervasiveness in the public sphere. Key questions explored are centered around how fatphobia impacts women in Western society, how fatphobia is created and maintained, and the exploration of where fatphobia is most pervasive in a person’s life. The study will analyze its research findings through a feminist and intersectional theoretical perspective. Some of the key findings in this study were that fatphobia is largely connected to patriarchy, Western culture, and colonization. As well as classism and neoliberalist ideologies and how these ideologies create and maintain fatphobic beliefs. The intersection between fatphobia and race was explored, however, there was a significant lack of perspective in the literature from fat women of colour. \nAdditionally, analysis on the biomedical discourse around obesity and weight discrimination was explored, eliciting extreme discrimination against fat bodies. Based on this information, it is apparent that awareness of fatphobia needs to be explored further in professional research and in day-to-day life. Specifically, recognizing fatphobia as a form of marginalization is recommended to be included in social work education and implemented into social work practice to ensure more inclusive knowledge and practice.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".