Consumers’ attitudes towards and acceptance of 3D printed foods in comparison with conventional food products
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary Little research currently exists in the literature on consumers’ perceptions of 3D printed foods; thus, present research aimed to investigate this topic. Two focus groups ( n = 8 and 12) were conducted to identify what consumers believe about 3D food printing. Responses from the focus groups were used to create an online survey investigating consumer attitudes towards 3D printed foods in comparison with conventional foods, as well as consumer beliefs about 3D food printing. Based on the results of the survey, three clusters were identified: the markedly interested cluster ( n = 140), moderately interested cluster ( n = 98) and the not interested cluster ( n = 91). The markedly interested cluster wanted to know more about 3D printed food, and believed it could reduce the cost of food and benefit people in the future. The moderately interested cluster was excited to try printed food. Conversely, the third cluster believed 3D printed foods were unacceptable and not safe to consume.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it