Validation of the Foodie Index and characterization of foodies across four countries
Bibliographic record
Abstract
Purpose This paper aims to determine the validity and performance of the Foodie Index in four countries in the Global North and describe the sociodemographic characteristics of foodies and their hedonic experiences of food. Design/methodology/approach About 824 adults from England, Canada, the USA and Australia, representative of age and region for each country, completed the 12-item Food Index (nine-point Likert scale) and answered a range of questions related to sociodemographics and pleasure derived from eating (seven-point Likert scale). Findings The internal reliability of the Index was very good (Cronbach’s alpha and mean interitem correlation), and confirmatory factor analysis yielded a four-dimensional solution, with Index items loading well onto separate factors describing monetary investment, enjoyment and interest, time investment and knowledge, respectively. The proportion of foodies in the populations sampled does not vary between countries p(χ2 > 0.05), suggesting a robustness in the foodie construct and the utility of the Foodie Index for determining foodie status. Foodies are significantly younger (17 years) than non-foodies (p(K < 0.05) and more likely to be university educated than are non-foodies for English and Canadian participants p(χ2 < 0.05). Additionally, foodies derive more pleasure from eating food relative to other regular daily activities and rate pleasure from both sensory and social experiences when eating higher (p(K < 0.05). Originality/value These findings support the use of the Foodie Index in further consumer research in the Global North and offer insights into the development and marketing of food services and products for specific consumer segments.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".