Unmasking the Cheerleader Effect: Body size perceptions among individuals presented in groups compared to alone
Bibliographic record
Abstract
The “Cheerleader Effect” (CE) describes how individuals’ faces are perceived as more attractive when presented in groups alongside similar faces, compared to alone. Other than faces, the CE emerges with full-bodies when individuals and groupmates are presented alongside similar body sizes and when rating perceived attractiveness. However, it remains unknown if the CE emerges with full-bodies and when rating perceived body sizes, or how differences in body size between individuals and groupmates impacts this effect. As such, this study examined (1) if ratings of perceived body size are impacted by individuals appearing alone or in groups, and (2) how this effect is impacted by differences in body sizes between individuals and groupmates. Nine women (Mage = 21.5 years) rated the perceived body size of 15 same-gender model images (3 models by 5 body sizes). Participants responded on a continuous scale from “very thin” to “very heavy” after models were presented alone or in groups with similar, thinner, or heavier groupmates of increasing body size differences. Women perceived individuals as thinner when presented alongside heavier (p
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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".