Understanding Consumer Attitudes toward Cultured Meat: The Role of Online Media Framing
Bibliographic record
Abstract
The widespread commercialization of cultured meat, produced from animal stem cells grown in vitro, faces significant challenges related to technical, regulatory, and social acceptability constraints. Despite advancements in knowledge, the acceptance of this innovation remains uncertain. Understanding individuals’ decision-making processes and interpretative patterns is crucial, with media framing playing a key role in shaping attitudes toward cultured meat adoption. This research, focusing on Twitter as a social media platform, examines the impact of media framing on consumer attitudes (cognitive, affective, and conative) regarding cultured meat. Qualitative (content analysis) and quantitative (MANOVA) analyses were conducted on 23,020 posts and 38,531 comments, selected based on media framing or containing relevant attitude components. This study reveals that media-framed posts significantly influence consumer attitudes compared to non-media-framed posts. While different types of media framing (ethical, intrinsic, informational, and belief) exhibit varying impacts on attitude components, posts combining ethical, intrinsic, and informational frames have a more substantial effect on cultured meat acceptability. The belief frame, particularly for the behavioral component, is equally influential. Consumer attitudes toward cultured meat are found to be ambivalent, considering the associated benefits and risks. Nevertheless, the affective component of attitude is notably influenced by posts featuring informational and ethical media frames. This study suggests implications for authorities and businesses, emphasizing the importance of differentiated education and marketing strategies. Advertising messages that combine ethical, intrinsic, and informational frames are recommended. Additionally, this study advocates for regulatory measures governing the production, marketing, and consumption of cultured meat to instill consumer confidence in the industry. By highlighting the significance of beliefs in cultured meat consumption behavior, this research points toward potential exploration of cultural and religious influences in future studies.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| 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".