Real-world convenience shapes laboratory food choices even when irrelevant
Bibliographic record
Abstract
While people report many factors that determine their food choices, research into how and when people integrate these factors into their decision process often focus on taste and health. Other attributes, such as convenience, have yet to be considered in this value-based framework, as common laboratory food choice paradigms often render convenience irrelevant. In two studies, we found that convenience can, and should, be studied in this value-based framework. Study 1 (N=118) used a thought-listing paradigm, identifying convenience (22.7%) as a frequently-considered thought during food choice, after taste (43.7%) and health (32.8%). Importantly, in study 2 (N=94) we found that convenience (β=0.06, p<.001) predicted lab-based food choice, even after controlling for taste (β=0.40, p<.001) and health (β=0.08, p<.001). We demonstrate that convenience should be considered as an important attribute in value-based studies of food choice.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".