Portion‐size preference as a function of individuals' body mass index
Bibliographic record
Abstract
OBJECTIVE: Large portions of food are often blamed for rising rates of obesity. We tested the possibility that people who are heavier may tend to select or prefer larger portions than do people who are lighter. METHODS: = 798) were asked to choose between a small and larger portion of pasta for a hypothetical meal (Studies 1, 2 and 4), to indicate their ideal portion from a range of portion-size options (Study 2), or to select their preferred portion size from each of 28 portion pairs (Study 3). RESULTS: = -0.06 to 0.33). The pattern was the same regardless of whether we grouped participants as having a body mass index (BMI) <25 vs. ≥25, as having a BMI of <30 vs. ≥30, or treated BMI as a continuous predictor. CONCLUSIONS: Given the lack of association between BMI and portion-size preference, we suggest that factors other than portion size, such as differences in meal frequency, food type, plate clearing or compensation at subsequent meals, may need to be considered in order to explain the increasing prevalence of obesity.
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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 | 0.005 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".