Estimating the effects of nutrition label use on <scp>C</scp>anadian consumer diet‐health concerns using propensity score matching
Bibliographic record
Abstract
Abstract The overarching goal of nutrition labelling is to transform intrinsic credence attributes into searchable cues, which would enable consumers to make informed food choices at lower search costs. This study estimates the impact of nutrition label usage on Canadian consumers’ (n = 8,114) perceived diet‐health concerns using alternative propensity score matching (PSM) techniques. We apply a series of tests and sensitivity analyses to overcome issues of endogeneity and selection bias frequently found in studies of diet‐health behaviour and to validate the impact of exposure to nutrition facts labels for users vs. non‐users. Our results support the notion that consumer uncertainty and related food‐health concerns are linked to their information behaviour, but not in straightforward manner. Dominant subjective food attributes, such as taste, convenience and affordability, may in fact outweigh the benefits of information about healthier, alternative food choices. In order to change dietary health behaviour, food manufacturer and policy makers alike need to adopt communication instruments that better account for differences in preferences, shopping habits and overall usage patterns of nutrition labelling information.
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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 | 0.025 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".