Who are you trying to fool: does weight underreporting by dieters reflect self-protection or self-presentation?
Bibliographic record
Abstract
Nutritionists are well aware that people tend to underreport their weights, but psychologists still often rely on weight self-reports. The present paper reviews research on weight underreporting and attempts to identify its underlying motivations. Restrained eaters (and overweight individuals) are especially likely to underreport their weight. We examine potential reasons for such underreporting in these groups, including (1) perceptual biases that make people misperceive body weight; (2) an impression-management/self-presentation strategy (telling others that one has a more socially desirable weight); or (3) self-protection, with underreporting allowing one to protect self-esteem by convincing oneself that one is thinner than is really the case. The evidence indicates that overweight and restrained women underreport their weight in an attempt to protect themselves. The consistent and motivated underreporting of weight by restrained eaters not only illuminates their psychological functioning, but indicates a bias that may be problematic for research that relies on self-reports.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.002 | 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".