Dinner Unboxed: Who Uses Meal Kits and Why? Findings from the International Food Policy Study
Bibliographic record
Abstract
Meal kits are becoming increasingly popular, particularly following the global COVID-19 pandemic which saw restrictions on the food industry. Meal kits deliver convenient pre-portioned and ready-to-cook ingredients for set recipes to consumers’ homes, facilitating in-home consumption. However, few studies have examined the frequency of using meal kits and the profile of individuals who purchase meal kits using population-level data. This study will examine meal kit use across five countries (Canada, the United States, the United Kingdom, Mexico, and Australia), and assess individual correlates of use, including: food skills, sustainability efforts, weight change efforts, healthy eating efforts, and prepared food consumption.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.006 |
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; both teacher heads agree on what is shown here.
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".