Price determinants of bred heifers: Do reputations matter?
Bibliographic record
Abstract
Abstract Replacement brood cows are among the most significant investments for cow‐calf operations, thus crucial to profitability. Many cow‐calf producers find it cost effective to purchase replacements from a reliable replacement heifer seller, though by doing so they increase risk of reproductive inefficiency due to unknown characteristics of the heifers. When important information about a product is missing to buyers, a seller can build a reputation over time that acts as signal for quality. Previous work has explored reputation effects in feeder cattle markets, but to our knowledge we are the first to explore reputation effects in bred replacement cattle markets. Using data from an annual replacement heifer sale, we analyze the values of heifer characteristics and test for premiums from reputation development. After controlling for reproductive practices, breed, and other characteristics, we find reputation does not play the role that Shapiro theorized. In this sale, the lot order is strategically chosen and may indicate bred heifer quality to buyers, replacing the need for reputation as a signal. This study highlights the importance of quality signals and regional preferences in bred replacement cattle marketing and lays the empirical groundwork for future studies to test Shapiro's theory.
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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 | 0.012 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".