Thin-Ideal Images and Affect: An Investigation Using Magazines and Minimizing Demand
Bibliographic record
Abstract
Women and girls generally experience slightly reduced satisfaction with their appearance following exposure to thin-ideal media images. Inconsistent findings have been obtained regarding the impact of such images on positive and negative affect. However, in experimental studies, researchers have typically exposed participants to a concentrated dose of such images, isolated from context, instead of showing them in an everyday context such as within a fashion magazine. This has implications for external validity because the context in which thin-ideal images are viewed may change their effects. Concentrated exposure also increases demand characteristics. The present study investigated the effect of thin-ideal images presented in magazines on viewers’ affect, while manipulating the level of demand characteristics. Undergraduate female students (N = 172) were assigned to three groups (Implied Demand, Minimized Demand, and Control), two of which were exposed to fashion magazines; the third group was exposed to control magazines containing no thin-ideal images. Demand characteristics were manipulated when the experimenter provided the magazines during a putative break period, and participants’ affect was assessed both pre- and post-exposure. Our results indicated that exposure to fashion magazines was no different from exposure to control magazines in the effects on participants’ affect.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".