Seasonality effect on US household demand for different beef cuts
Bibliographic record
Abstract
Australia is one the largest exporters of beef and beef products to the United States (Haley & Jones, 2017). A better understanding of the American demand for beef is important since Australia is facing strong competition from Canada and New Zealand in the beef market. We applied a discrete choice experiment to investigate 946 American consumer preferences and willingness-to-pay (WTP) for different beef products. Consumers were presented with a novel experiment in which they indicated “how many” they would purchase for ground, diced, roast, and six cuts of steaks (sirloin, tenderloin, flank, flap, New York and cowboy/rib-eye). The results from a scaled adjusted ordered logit model showed that after price, cues related to safety option purchases such as certified logo, type of packaging, antibiotic free and organic products play a stronger influential role on American consumers’ decision making (especially in summer where the opportunities for foodborne bacteria to thrive in warm weather is higher) compared to other beef attributes.Furthermore, on average US consumers purchase diced and roast products more often in winter “as a slow cooked season” than in summer whereas New York strip and flank steak are more popular in summer as “the grilling season” than in winter. Finally, this study provides managerial and policy implication and recommendations to help Australian exporters to better understand US consumer preferences for beef through an understanding of seasonal effects on demand for this good.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".