How do you like me now? Body representation in young girls exploring the resonance effects of the new barbie dolls
Bibliographic record
Abstract
There are well-documented negative body image effects related to the unrealistic body shape of the "original" Barbie doll. Matel® has recently produced new Barbie dolls with "tall", "curvy", and "petite" body types, yet the impact of these new body types has not been studied on young girls' body images. The present research was conducted to investigate youth's internalization of the different Barbie representations, as well as participants' explicit preferences toward different Barbie body types. Participants (N=38, Mage=10, SD=2.24 years) completed a body-part compatibility task to evaluate how each individual implicitly related their body to different doll images, and an online questionnaire assessing explicit preferences for each body type. Based on the results, there were significant (p < .05) body-part compatibility effects for the original, curvy and petite dolls, but not for the tall Barbie. The effects were not modulated by age or ethnicity. These findings indicate that participants' internal representations of their own body matched all doll images except the doll with the tall, thin body type. This pattern of compatibility effects was not consistent with the explicit measures (e.g., curvy Barbie was most likeable but reported as least desirable, whereas original and tall Barbie were both rated low on likeable, but high on desirable). Overall, these data indicate that the manner in which young girls implicitly resonate and identify with dolls with different body types is not consistent with their explicit preferences and these findings may have body image implications for the internalization of body ideals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".