Exposure to thin and non-thin bodies elicits ‘feeling fat’: Validation of a novel state measure
Bibliographic record
Abstract
‘Feeling fat’ refers to the subjective experience of carrying excess weight and relates to severity of eating pathology. Despite research suggesting that ‘feeling fat’ fluctuates across contexts, this construct is almost exclusively assessed in terms of frequency or as a trait. Examining state ‘feeling fat’ in response to external stimuli can inform us of the nature of this construct. In an experimental study, 290 community women were exposed to five categories of affective (pleasant, aversive, and neutral) and body (thin and non-thin) images in quasi-random order. Self-Assessment Manikin (SAM) valence and arousal rating scales as well as a novel SAM ‘feeling fat’ scale were rated for each image. Theoretically-relevant constructs (i.e., trait ‘feeling fat’, thin-ideal internalization, body dissatisfaction, eating pathology) were also measured. Body images elicited greater state ‘feeling fat’ than affective images, with images of non-thin bodies producing higher state ‘feeling fat’ than thin bodies. Positive correlations were observed between state ‘feeling fat’ in response to thin and all variables of interest, whereas associations between these variables and ‘feeling fat’ in response to non-thin images were small or non-significant. The development of a state measure of ‘feeling fat’ allows for the investigation of triggers of this bodily experience and will facilitate future research.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".