What is a binge? The influence of amount, duration, and loss of control criteria on judgments of binge eating
Bibliographic record
Abstract
OBJECTIVE: We investigated the influence of amount of food eaten, duration of eating episode, and loss of control in judgments of eating episodes as binges. METHOD: Participants rated the degree to which the eating behavior of a female actress qualified as a "binge" after observing eight videotaped vignettes in which the amount of food eaten, apparent duration of eating episode, and loss of control were varied. Binge ratings were stable across a test-retest interval of 3-4 weeks, there was minimal observer drift, and the experimental variables were independently perceived. RESULTS: A repeated measures analysis of variance (ANOVA) on binge ratings revealed significant main effects for quantity and loss of control, and a significant Quantity x Time interaction. DISCUSSION: The results are consistent with the definitional criteria of a binge, underscore the independence of loss of control, and highlight the importance of the violation of dietary standards in judgments of binges. Moreover, they illustrate the reliability and sensitivity of the methodology, and its potential for further investigations of binge eating.
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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 | 0.003 | 0.030 |
| 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) | 0.001 | 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".