Flaunting fat and sharing fashion: A multimodal analysis of how two Black fatshion influencers resist weight stigma on Instagram
Bibliographic record
Abstract
Fat fashion blogging has largely been celebrated for its resistance potential. Historically, much of this blogging was collaborative with a focus on sharing information and counter-aesthetic images on plus-size fashion. With the rise of the advertising-driven social media platform Instagram, individuals can capitalize on fatshion blogging by becoming social media influencers who promote brands and encourage purchasing decisions. This article uses visual semiotic analysis and critical discourse analysis to show how two fat Black fashion influencers use fashion, flaunting and fat discourse to resist weight stigma. We argue that these efforts complicate our understanding of fat resistance due to the neo-liberal intensification of the entrepreneurial self. Exploring the fat fashion Instagram phenomenon opens new avenues for reflecting on digital resistance to weight stigma and how this is undermined by capitalist interests.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".