Cultivating change in food consumption practices: The reception of the social representation of alternative proteins by consumers
Bibliographic record
Abstract
This article is concerned with the dynamics of change in protein consumption practices from the perspective of the consumer. It is based on a model, informed by social representation theory, that aims to understand the role played by various types of representation of alternative proteins in the process of changing food consumption practices. It discusses the reception, by consumers, of the representations associated with alternative proteins on Instagram. Methodologically, three focus groups were organized with different consumer segments (omnivorous, flexitarian and vegetarian and vegan consumers), as well as seven individual interviews. Participants were submitted to the social representations of alternative proteins, and visual stimuli from social media were mobilized for this purpose. Results show that the publications which boast the environmental, animal welfare or health attributes of alternative proteins generally contribute to the cultivation of new elements of practices. While this kind of publications is essential to help consumers question their established practices linked to meat and dairy consumption, they can also generate a critical reception that is not conducive to change, making them a double-edge sword. Publications that relate to the representations involved in daily food consumption proteins (e.g. that alternative proteins are versatile and crowd-pleasing) emerge as being safer in terms of reception, although as standalone they may not be able to achieve a deep level of change in food consumption practices. The results of this study show the importance of deploying a diverse communication strategy about alternative proteins that appeal to a variety of consumer segments.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".