Social Models Provide a Norm of Appropriate Food Intake for Young Women
Bibliographic record
Abstract
It is often assumed that social models influence people's eating behavior by providing a norm of appropriate food intake, but this hypothesis has not been directly tested. In three experiments, female participants were exposed to a low-intake model, a high-intake model, or no model (control condition). Experiments 1 and 2 used a remote-confederate manipulation and were conducted in the context of a cookie taste test. Experiment 3 used a live confederate and was conducted in the context of a task during which participants were given incidental access to food. Participants also rated the extent to which their food intake was influenced by a variety of factors (e.g., hunger, taste, how much others ate). In all three experiments, participants in the low-intake conditions ate less than did participants in the high-intake conditions, and also reported a lower perceived norm of appropriate intake. Furthermore, perceived norms of appropriate intake mediated the effects of the social model on participants' food intake. Despite the observed effects of the social models, participants were much more likely to indicate that their food intake was influenced by taste and hunger than by the behavior of the social models. Thus, social models appear to influence food intake by providing a norm of appropriate eating behavior, but people may be unaware of the influence of a social model on their behavior.
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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 | 0.002 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".