Including sustainability factors in the derivation of eater profiles of young adults in Canada
Bibliographic record
Abstract
Purpose The purpose of this research is to (1) identify factors affecting food choices of young adults in Canada based on environmental perceptions, personal and behavioral factors as determinants of eating behaviors; (2) segment Canadian young adults based on the importance of the identified factors in their food choices. Design/methodology/approach An online survey was administered to Canadians aged between 18 and 24 to collect data on socio-demographic factors and eating behaviors (N = 297). An exploratory factor analysis (EFA) was used to identify the main factors affecting eating behaviors in young adults, followed by K-means clustering to categorize the respondents into consumer segments based on their propensity to agree with the factors. Findings Six factors were extracted: beliefs (ethical, environmental and personal); familiarity and convenience; joy and experience; food influencers and sociability; cultural identity; and body image. Using these factors, six consumer segments were identified, whereby members of each segment have more similar scores on each factor than members of other segments. The six consumer segments were: “conventional”; “concerned”; “indifferent”; “non-trend follower”; “tradition-follower”; and “eat what you love”. Originality/value Identifying major factors influencing eating behaviors and consumer segmentation provides insights on how eating behaviors might be shaped. Furthermore, the outcomes of this study are important for designing effective interventions for shaping eating behaviors particularly improving sustainable eating habits.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".