Gene-Edited Meat: Disentangling Consumers' Attitudes and Potential Purchase Behavior
Bibliographic record
Abstract
Novel gene-editing (GE) technologies provide promising opportunities to increase livestock productivity and to tackle several global livestock production sustainability and food security challenges. However, these technologies, as with previous genetic modification technologies in food production, are very likely to generate social controversy and opposition toward their use in the meat industry. Here, we explored public attitudes and consumption predisposition toward gene-edited meat products and their potential added benefits to livestock farming. Our results show that societal perception currently comes as a package, where the use of gene-editing technology acts as an extrinsic cue of meat products quality, and is used to make a range of inferences about all quality facets at once. Although consumers with anti-GE attitudinal positions generally were not sensitive to price discounts or added benefits, added benefits increased the consumption predisposition of most moderate and pro-GE consumers, where benefits related to animal welfare had larger effects than those relating to the environment or human health issues.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".