Endorsing fitspiration media: Examining differential predictors of fitspiration believability for men and women
Bibliographic record
Abstract
Fitspiration (Fitspo) is a social media trend that uses photos and quotes to depict idealized bodies as desirable and achievable through extreme exercise and eating practices. Exposure to, and high believability of fitspo media is associated with negative affect and body dissatisfaction. When internalized, the extremes promoted in fitspo may encourage exaggerated or biased thinking (i.e., cognitive errors). It is unclear whether cognitive errors are related to or impact how fitspiration is perceived. The purpose of this study was to examine whether fitspo-related cognitive errors (FCEs), explicit and implicit evaluations, and their interactions positively predict the believability of fitspo media. Self-identified women (N=136, M=22.46, SD=3.34) and men (N=152, M=24.25, SD=4.0) between the ages of 18 to 65 completed an online survey measuring demographics, FCEs, and physical activity. Participants were shown fitspo media, rated its’ believability, and stated up to five thoughts they had about the media (coded into anti- or pro-fitspo comments). Finally, they completed an implicit association task that paired fitspo with “good” or “bad” descriptors. Men and women were shown gender-specific fitspo and were analyzed separately. For both, explicit pro and anti-message comments significantly predicted the believability of fitspo messages, but their interactions did not (R2men=.27, p
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".