Accuracy in the estimation of body weight: An alternate test of the motivated‐distortion hypothesis
Bibliographic record
Abstract
OBJECTIVE: Inaccuracies in self-reported weight are believed to represent a motivated distortion, but cognitive or perceptual biases have not been excluded. We examined the ability of participants to estimate the weight of a target person as a means of distinguishing between motivated distortions and perceptual biases. METHOD: Participants (restrained eaters and unrestrained eaters; women and men) estimated the weight of a target individual, which was compared with the actual weight of the target individual. RESULTS: Restrained and unrestrained eaters did not differ in their estimates of the target's weight, and men underestimated the target's weight to a greater extent than did women. DISCUSSION: The pattern of inaccuracies observed does not parallel those found in research on self-reported weight. This observation suggests that perceptual biases do not explain inaccuracies in self-reported weight and that such inaccuracies may be the result of motivated distortions. Issues regarding data analysis and presentation are also discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".