Cost Efficiency for Alberta and Ontario Dairy Farms: An Interregional Comparison
Bibliographic record
Abstract
In this study, two non‐homothetic translog stochastic meta‐frontier cost functions—with and without local concavity imposed—are estimated using a nonlinear maximum likelihood estimation procedure to compare the cost efficiency of Alberta and Ontario dairy farms for the period 1984–96. The resulting cost efficiency estimates are not very sensitive to whether or not curvature is imposed. In contrast, the properties of the cost and input demand functions (e.g., elasticities) are sensitive to imposition of local concavity during estimation. The implication is that if an inappropriate model that does not satisfy the properties required by the economic theory is used, the estimated input demand functions may not be reliable. Average cost efficiency for the pooled sample, with local concavity imposed, is approximately 89%. This suggests some potential for improved performance in the sector. The results also suggest that Ontario dairy farms may be more cost efficient than Alberta dairy farms, but the statistical evidence is inconclusive.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".