Women's reactions to body positivity posts vary by posters' race and body size
Bibliographic record
Abstract
The online body positivity movement focuses on representing and supporting those with marginalized bodies, particularly fat women and women of color. Despite the popularity of body-positive posts on Instagram, no research has examined how the race and body size of women featured in the posts affects users’ reactions. Across four experiments (total n = 2113), young women (aged 18-30) in the U.S. were randomly assigned to rate a body positivity Instagram post featuring either a Black or White model who was either fat or thin. Study 1 indicated participants preferred body positivity posts featuring women with marginalized bodies (i.e., Black and/or fat). We replicated these findings with a new sample (Study 2), a new set of images (Study 3), and with a sample of Black and White women to examine the effects of participant race on reactions to the posts (Study 4). Results suggested that in the context of body positivity posts, women preferred posts featuring women with marginalized bodies over posts featuring thin, White women. Despite the proliferation of anti-Black and anti-fat attitudes in online spaces, these studies suggest women prefer to see body positivity posts that center women with marginalized bodies. • In body positivity faux Instagram posts, young women preferred to see images of Black and/or fat women over images of thin, White women. • Posts featuring thin, White women were better received when the caption was focused on body inclusivity compared to self-acceptance. • Young Black women showed similar preferences after viewing images of thin, Black models as they did for images of fat models. • Across four studies, women showed a preference for body positivity faux Instagram posts that featured marginalized bodies.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".