Bibliographic record
Abstract
• Foodies are more food literate than non-foodies. • Foodies have greater food disgust than non-foodies. • Food involvement and foodiness capture different dimensions. • The Foodie Index assists with consumer characterisation and market segmentation. • Results inform initiatives to increase food literacy. Foodies, despite representing a significant and important consumer segment globally, are not well characterized in the scholarly literature. In this study we examine the association between foodiness and food literacy, involvement, and disgust; factors known to mediate several dimensions of food behaviour. A sample of 617 Canadian youth (18–25 yrs.) completed a 25-item food literacy scale, the 8-item Food Disgust Scale (English version), and the Food Involvement Scale. Foodiness and foodie status (foodie or non-foodie) were determined using the modified Foodie Index. Results show that foodies ( n = 204) are more food literate overall than non-foodies ( n = 203) (Kruskal-Wallis H test), and score higher across all five subscales. They also have a higher level of general education attainment (Chi-square test). Unexpectedly, foodies also display higher food disgust than non-foodies (Kruskal-Wallis H test). We also show that the foodie construct is more complex than and qualitatively distinct from food involvement. Our findings should assist food marketers and retailers in market segmentation initiatives, and in aligning their products and services with the features and needs of those segments. Additionally, we discuss implications for education and policy strategies aimed at improving food literacy, and identify future research needs.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".