The Role Of Labelling In Consumers’ Functional Food Choices
Bibliographic record
Abstract
Given the credence nature of functional food attributes labelling plays a key role in allowing consumers to make informed choices about foods with enhanced health attributes. The degree to which a particular jurisdiction permits health claims for food products and the type of allowable health claim influence the information set available to consumers. In Canada the regulatory environment governing health claims for functional food products is somewhat more restrictive than in other jurisdictions, including the United States. Food manufacturers therefore also use visual imagery to suggest a health benefit, such as the picture of a red heart to imply that a product has heart health benefits. The paper characterizes these labelling strategies as “partial labelling”, while “full labelling’’ refers to formal health claims on food labels, ranging from general (structure-function) claims, to risk reduction claims, to disease prevention claims. This paper explores the effect of labelling (full and partial) on consumers’ functional food choices. How might different types of labelling information and the verification of health claims by different agencies affect consumers’ preferences for functional foods? Using data from an online survey of 740 Canadians conducted in summer 2009 the paper uses discrete choice modelling to examine the responses of Canadian consumers to different product labelling strategies for milk enhanced with Omega-3. Conditional Logit and Latent Class models are estimated. Preliminary results suggest that full labelling is preferred over partial labelling, but primarily for risk reduction claims. There is no significant difference between a function claim, such as “good for your heart” and partial labelling in the form of a red heart symbol. The choice experiment included verification of health claims by a government agency (Health Canada) or by a third party (Heart and Stroke Foundation). The preliminary results suggest that consumers on average respond positively to verification of health claims, however, the latent class model reveals considerable heterogeneity in consumer attitudes toward the source of verification. Interactions between key-socio-demographic and attitudinal variables and the main effects variables in the choice experiment provide further insights into consumer responses.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".