Plant-based foods in Canada: information, trust and closing the commercialization gap
Bibliographic record
Abstract
Purpose Despite the growing awareness of links between meat consumption and human, animal and environmental health, consumption rates of protein rich plant-based foods (PBFs) in Canada remain relatively low. The purpose of this paper is to better understand how information sources and trust relate to PBFs in Canadians’ diets, and how these variables may factor into closing the commercialization gap for PBFs in Canada. Design/methodology/approach A geographically representative sample of Canadians ( n =410) participated in a 20-minute, online survey. The survey consists of 24 questions covering demographic characteristics, motivations behind current and future food choices, frequency of current PBF consumption, sources of information about PBFs and trust levels of these sources. Findings Most Canadians get information about PBFs from labels, the internet and family and friends, but only half trust these sources to provide accurate information. Sources of information (e.g. licensed health care professionals) that rank high in trust are only consulted by a minority of respondents. Several information sources (e.g. family and friends) are associated with consumers’ willingness to try new PBFs, whereas other sources (e.g. labels) are associated with Canadians being unlikely to try new PBFs. Originality/value Understanding the patterns of where Canadians access information about PBFs and what sources of information are trusted can help to strategically place information about PBF qualities among select information sources and to remove some of the information barriers contributing to the PBF commercialization gap.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".