Add to cart: an investigation of the efficiency of social comparisons to thin-ideal images in the context of online shopping
Bibliographic record
Abstract
Recently, researchers have investigated the cognitive efficiency of social comparisons young women make to thin-ideal images. However, results have been mixed and methodology problematic (e.g. low ecological validity, lack of consideration for ethnicity). The purpose of the present study was to address these issues. Ninety-six Caucasian undergraduate females were exposed to thin-ideal images. These images were presented in the context of an online shopping experience, to create a more ecologically valid context. To investigate cognitive efficiency, cognitive load was manipulated by having participants memorize the colours of the models’ clothing items. Participants did not experience a decrease in appearance satisfaction when under high cognitive load, suggesting inefficiency. In contrast, an observed increase in negative mood under high load conditions may suggest efficiency. However, potential alternative explanations for this latter result include the non-specificity of the mood measure, the context the images were presented in, and task difficulty
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".