Are we aware of the external factors that influence our food intake?
Bibliographic record
Abstract
OBJECTIVES: This research examines the extent to which people accurately report some of the external influences on their food intake. DESIGN: In two studies, specific factors (the presence and behavior of others) were manipulated in order to influence the amount of food that individuals consumed. MAIN OUTCOME MEASURES: The main outcomes of interest were participants' spontaneously generated explanations for their food intake (Study 1; n = 122), and their ratings of the importance of several potential determinants of food intake (Study 2; n = 75). RESULTS: In Study 1, there was high concordance between the amounts eaten by members of a dyad, but very few participants indicated that they were influenced by their partner's behavior; they instead identified hunger and taste as the primary determinants of intake. Study 2 showed that participants' intake was strongly influenced by the behavior of others, but people rated taste and hunger as much more important influences on their intake. CONCLUSIONS: If external environmental factors influence people's food intake without their awareness or acknowledgment, then maintaining a healthy diet can be a challenge, with long-term consequences for health and well-being.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".