A thematic content analysis of #cheatmeal images on social media: Characterizing an emerging dietary trend
Bibliographic record
Abstract
Despite the pervasive social endorsement of "cheat meals" within pro-muscularity online communities, there is an absence of empirical work examining this dietary phenomenon. The present study aimed to characterize cheat meals, and explore the meaning ascribed to engagement in this practice. Thematic content analysis was employed to code the photographic and textual elements of a sample (n = 600) that was extracted from over 1.6 million images marked with the #cheatmeal tag on the social networking site, Instagram. Analysis of the volume and type of food revealed the presence of very large quantities (54.5%) of calorie-dense foods (71.3%) that was rated to qualify as an objective binge episode. Photographic content of people commonly portrayed highly-muscular bodies (60.7%) in the act of intentional body exposure (40.0%). Meanwhile, textual content exemplified the idealization of overconsumption, a strict commitment to fitness, and a reward-based framework around diet and fitness. Collectively, these findings position cheat meals as goal-oriented dietary practices in the pursuit of physique-ideals, thus underscoring the potential clinical repercussions of this socially-endorsed dietary phenomenon.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".