A conceptual framework for analyzing consumers’ food label preferences: an exploratory study of sustainability labels in France, Quebec, Spain and the US
Bibliographic record
Abstract
In a qualitative study of 375 consumers in France, Quebec, Spain and the US, respondents are asked to choose between pairs of actual food labels and to describe the reason(s) for their choice. The food labels included sustainability labels (eco-labels, Fair Trade, origin) as well as product attribute (e.g. quality, kosher) and health/nutrition labels. Respondents' reasons were coded in the original language using the same coding system across all four nations to examine their preferences for label message, design and source. We also examined the role of consumers' values, beliefs and experiences on their label choices. The coding system was drawn from a review of theoretical and empirical literature and provides a conceptual framework we call the Label Consumer Interaction model for evaluating consumers' food label preferences. Although this is case study, the results point to substantial differences across nations in terms of preferred labels, as well as the rationale for their choice in terms of attributes of the labels and consumer characteristics.
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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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".