Am I sexy; do I know it? Does the thin ideal in pop music lyrics affect body image in physically active women?
Bibliographic record
Abstract
Media exposure to the thin ideal has been shown to have a negative impact on body image. Viewing music videos that depict images of the thin ideal is associated with increased body dissatisfaction in women. However, whether these effects are due to the images in the videos or the lyrics in the songs is unknown. This study aimed to explore the effects of music lyrics on body image in physically active female university students. A repeated measure design was used; participants participated in three sessions, where they listened to a different playlist of current pop music. The playlist consisted of music with positive body image messages, negative body image messages, and no reference to body image. Following listening to each playlist, participants completed measures of body image and affect assessing social physique anxiety, body dissatisfaction, and positive and negative affect. Results showed on average positive affect was higher after listening to the positive music compared to the negative music and control condition, and on average women felt less fat following the positive condition compared to the negative and control condition. Lyrics that promote all body shapes sizes, may lead to more positive psychological outcomes, however further research should be completed to explore the effects music has on body image outcomes in other samples, such as less active women.
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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 | 0.000 | 0.003 |
| 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) | 0.003 | 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".