Cultured Meat Consumer Acceptance: Addressing Issues of Eco-Emotions
Bibliographic record
Abstract
Meat substitutes, in particular cultured meat, appear to be an effective way of combating climate change, ensuring human and animal welfare, and meeting the challenges of food security. In the face of the climate emergency, we need to speed up the decarbonization of local and national economies and curb populations’ negative emotional responses. Eco-emotions (such as fear) can indeed go so far as to cause disengagement from the environmental transition and hamper action. The aim of this article is to understand and predict, from the perspective of the extended theory of planned behaviour (TPB), (i) consumer intentions and (ii) the determinants of the adoption of cultured meat by introducing two important variables drawn from the literature on eco-emotions, i.e. eco-anger and eco-depression. The results show that, in addition to the traditional TPB variables (attitudes, subjective norms, perceived behavioural control), eco-depression has a significant effect on consumer intentions and the acceptability of cultured meat. This research can help to improve decision-making processes and to effectively predict intentions, acceptability, and purchasing behaviour with regard to cultured meat. Organizations will be able to use this model to propose differentiated marketing techniques, optimize marketing campaigns, and improve citizen engagement.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".