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Record W6989174028

An Analysis of Consumer Response to Plant-based Meat Alternative Labelling Policy

2022· dissertation· en· W6989174028 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2022
Typedissertation
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsnot available
Fundersnot available
KeywordsLabellingAgency (philosophy)AppealMeat packing industryConsumer demandConsumer choiceFood productsConsumer behaviour
DOInot available

Abstract

fetched live from OpenAlex

Plant-based meat alternatives, defined as products made with plant-based protein that imitate the taste, texture, and appearance of real meat, have been subject to rapid market growth in recent years. These products tend to appeal to consumers who are actively reducing their meat consumption, typically due to concerns about animal welfare, environmental sustainability, or health issues. The simulant nature of these products introduces the need for regulation of labels to facilitate informed consumer decision-making when selecting meat and plant-based alternatives at the grocery store. In Canada, guidelines exist which regulate the use of meat-related terms (e.g., burger, ground, etc.) on the labels of plant-based meat alternatives, nutritional content, and other aspects of these products. While meat-related terms are permitted in Canada, provided certain disclaimers are also present, some jurisdictions abroad have banned such labels entirely. In Canada, some meat industry groups have called for the removal of such terms, and in 2020 the Canadian Food Inspection Agency (CFIA) conducted a consultation on its guidelines for plant-based meat alternative labelling. Despite a dynamic policy environment, research that investigates the consumer demand effects of plant-based meat alternative labelling policy remains elusive. A survey of 1203 Canadian consumers was conducted to assess the consumer demand effects of different regulatory approaches to the use of meat-related terms on plant-based meat alternative labels. The survey included a discrete choice experiment, where respondents were assigned to one of three labelling treatments – unregulated labels, current Canadian regulations, and a meat-related terms ban. Choice sets featured ground beef and plant-based alternatives with varying attributes and prices. The choice experiment facilitated the investigation of two secondary research objectives: consumer response to regulated protein label claims, and an assessment of preference heterogeneity for plant-based meat alternatives under different labelling policy scenarios. The data was analyzed using multinomial logit, random parameters logit, and latent class logit models, eliciting marginal utility and willingness-to-pay estimates for the attributes and policy effects. Results show that the labelling policy environment does impact consumer preferences for ground beef and plant-based alternatives. Ground beef is preferred by most consumers in the Canadian market under all three labelling treatments. Further, consumers prefer meat alternatives in an unregulated market relative to the current Canadian regulations and the meat-related terms ban treatments. On average, consumers exhibit similar reductions in willingness-to-pay under the two regulated treatments. However, these effects diverge when preference heterogeneity is accounted for. Five classes of consumers were identified in the latent class logit model, with varying preferences, characteristics, and responses to labelling policy. Preferences for protein claims are generally strong and positive, and there is a significant degree of heterogeneity in preferences for products, attributes, and labelling policy frameworks. The analysis reveals numerous insights into both market and policy issues of plant-based meat alternative labelling. It is in the firm’s best interest to utilize meat-related terms on product labels. However, the disparity in preferences among policy treatments indicates that the provision of information in the form of label disclaimers alongside meat-related terms likely provides valuable information to consumers who may be confused or inattentive otherwise.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.680
Threshold uncertainty score0.643

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.005
GPT teacher head0.194
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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