Costly, confusing, polarizing, and suspect: public perceptions of plant- based eating from a thematic analysis of social media comments
Bibliographic record
Abstract
Introduction A key approach to fostering more sustainable food systems involves shifting dietary patterns towards increased plant-based eating. However, plant-based eating remains low among Canadians. The objective of this research was therefore to explore public perceptions of plant-based eating in a Canadian context. Methods A qualitative design was used to analyze social media comments posted on Canadian news source Facebook articles between January 16th, 2019 – July 16th, 2020. Investigating perceptions of plant-based eating on social media may capture a broader sample of the population than can be captured using other qualitative methods. Template analysis, a type of codebook thematic analysis, was used to generate themes and subthemes using NVivo software. Results Nine articles were selected for inclusion and a total of n = 4,918 comments were collected. Five themes and 19 subthemes related to plant-based eating were generated and presented with quotations. Themes included: (1) The ethics of food; (2) The affordability and accessibility of food; (3) Distrust of food system stakeholders; (4) Beliefs related to dietary behavior, health, and the environment; and (5) Sensory aspects of plant-based proteins. Discussion Findings suggest that addressing food affordability and accessibility, increasing public food literacy, using non-judgmental approaches, and increasing food system transparency and communication may be strategies to foster plant-based eating. Results of this study provide insight for the development of more effective public health messaging about plant-based eating and help inform future research and interventions to address barriers related to plant-based eating and promote consumption.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".