Rating of figures used for body image assessment varies depending on the method of figure presentation
Bibliographic record
Abstract
OBJECTIVE: To determine the effect of method of figure presentation on figure rating scales used for body image assessment. METHODS: Ratings of current, ideal, and socially acceptable body sizes were elicited from 360 university students. Male and female figure drawings ranging from thin to obese were presented to subjects using one of three presentation methods. Figures were presented as an ordered array, an unordered array, or they were placed individually on cards that were fixed in order from thin to obese. RESULTS: Figure ratings were significantly different among methods for the selection of current and ideal figure and socially acceptable body sizes. DISCUSSION: The method of figure presentation had an influence on figure ratings. These findings suggest that the manner in which figures are presented to research participants for the evaluation of body image constructs may be responsible, in part, for the discordant results reported in the body image assessment literature. Future research is required to address potential reasons why the method of figure presentation results in different figure ratings.
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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 | 0.020 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".