Looking beyond value‐based pricing of beef in North America
Bibliographic record
Abstract
Purpose Quality traits desired by consumers may not be adequately captured by beef industry standards associated with grid or value‐based pricing alone. Aims to demonstrate this shortcoming by examining strategies of selected companies in North America at the four supply chain levels of cow‐calf genetics, feedlot feeding, processing, and retailing that have been proactive in producing desirable beef attributes efficiently to better meet consumer beef demand. Design/methodology/approach The vertical alliance between Ralphs retailing, Sunland Beef processing, and a handful of feedlots using narrowly defined beef genetics are examined to illustrate how consumer market research and coordination throughout the supply chain may address many shortcomings associated with current value‐based pricing of beef criteria. Findings Better information sharing and coordination between seedstock and retail industries could help assure that consumer preferences of beef palatability and consistency are met while meeting high production efficiency standards. Practical implications Cow‐calf, feedlot, and packing industries need to better track and manage information flows of genetic‐management paths from consumer to seedstock producer in order for the beef industry to be more competitive. Originality/value Experiences of our case companies suggest that the beef industry will need to look beyond the North American grid or value‐based pricing of beef in order to maintain or improve market share with competing pork and poultry sectors.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".