Beef Producers Preferences for Joining Beef Alliances: An Arizona and Western Canada Study
Bibliographic record
Abstract
Three surveys were administered to cow-calf producers in Western Canada, Arizona, and Arizona Rancher Beef Quality Assurance (BQA) members. Two separate binary probit models were used to analyze the data. The first model estimates the willingness of beef producers to join an alliance and the second model estimates which alliance attributes producers willing to join an alliance prefer. Results from model one indicate that producer’s age has a negative effect on willingness to join an alliance, producer’s education has a positive effect, herd size has a positive effect, and if respondent is a Canadian or an Arizona Rancher BQA producer, it also has a positive effect. Results from model two indicate that producer’ data collection has a positive effect on attribute preferences in joining an alliance, alliance sale type has a positive effect, alliance restriction protocols has a positive effect, and herd size has a positive effect.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".