Embodiment Feels Better
Bibliographic record
Abstract
In a five-year longitudinal study, we investigated the role of body objectification in shaping girls' self-esteem and depressive symptoms over the course of adolescence. Multivariate Latent Growth Curve Modeling (MLGM) was used to test the association between body objectification and both self-esteem and depressive symptoms with data from 587 adolescent girls who began the study at age 13 and completed the study at age 18. Results revealed that body objectification decreased, self-esteem increased, and depressive symptoms remained relatively steady across adolescence. Girls who experienced decreases in body objectification also tended to increase in self-esteem and decrease in depressive symptoms over the course of adolescence, even after accounting for several factors known to be associated with positive youth development including race/ethnicity, socioeconomic status, educational achievement, religiosity, and body satisfaction. Practical implications for reducing objectification and enhancing girls' well-being through health, physical, and sexuality education, as well as through media literacy programs are discussed. Directions for future research are also discussed, including a greater focus on the role of race/ethnicity, research on boys, and the need for more experimental studies of body objectification.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".