Table_1_Costly, confusing, polarizing, and suspect: public perceptions of plant- based eating from a thematic analysis of social media comments.pdf
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 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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.066 | 0.004 |
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".