The effects of self-disclaimer Instagram captions on young women's mood and body image: The moderating effect of participants’ own photo manipulation practices
Bibliographic record
Abstract
The current experiment investigated the impact of attaching self-disclaimer captions (i.e., captions about whether photos had been edited) to thin-ideal Instagram photos on young women's body image and mood. Participants were 311 undergraduate students aged 18-25 years. Participants were randomly assigned to view images of a thin woman on Instagram with no captions, or with a generic, specific, or warning self-disclaimer caption, and completed pre and post measures of body image and mood and a questionnaire about their own photo-editing practices. Across all conditions, exposure to the images resulted in decreased body satisfaction, likelihood to compare one's body to another's, happiness, confidence, and anxiety. There was no significant effect of disclaimer type on body image or mood, and therefore no type of self-disclaimer had an ameliorating effect. However, specific disclaimers were superior to the other disclaimers at reducing likelihood to compare one's body to another's, for women high on photo manipulation. Future research should be conducted in adolescent girls and men.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".