Value added meat marketing around the globe:International insights on safety, health, and convenience
Bibliographic record
Abstract
In highly competitive meat markets it is important to offer value added products to consumers. Thus, we need to understand which attributes are especially valued by consumers. This track session will contribute to a better understanding of consumer preferences for value added meats across different countries and simultaneously address different stages of the food chain by acknowledging factors such as breeding, forage (fat content), meat cuts as well as product labelling and packaging. Comparing consumers’ choices for value added pork and beef across different countries is the main theme and focus of this session. All papers present current empirical studies from countries such as the U.S., Canada, Germany, Italy and Australia. We will discuss differences in consumer willingness-to-pay for taste versus health in Australia (beef steaks). Canadian consumers’ valuation of pork chops from different production practices will be examined and the economic and technological dimensions of meat packaging will be related to each other presenting work from the US and Germany. Lastly, we will uncover the competitive nature between different beef cuts for Italian consumers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".