The Disadoption of rbST and Its Economic Impact: A Switching Regression Approach
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This paper focuses on the disadoption of rbST and addresses two key questions related to rbST use and its effects on dairy profitability. First, what are the determinants of the disadoption decision, and do they differ from those of the adoption decision? Second, do the earnings of disadopters differ from those of current adopters? Using a nationally representative dataset of U.S. dairies from 2010, a bivariate probit model with partial observability and an endogenous switching model is estimated. Consistent with other studies, the results show that rbST use does not have a statistically significant effect on dairy profitability. However, within the group of producers who have adopted rbST, I present some empirical evidence that disadopters are doing worse off than those who are still using rbST.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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