Assessing Technical Efficiency of Québec Dairy Farms
Bibliographic record
Abstract
The purpose of this paper is twofold. Our first objective is to measure the level of technical efficiency of Québec dairy farms. Our second objective is to gauge the robustness of our results with respect to the selection of a functional form and of a distribution for the inefficiency index. We estimate efficiency frontiers for Cobb‐Douglas (C‐D), translogarithmic (TL) and generalized Leontief (GL) production functions with half‐normal, truncated normal and exponential distributions. Our results, based on likelihood dominance criterion (LDC) indicate that the GL production technology dominates the other two functional forms, and this ranking is robust to changes in the distribution of the inefficiency index. Efficiency scores and ranks are highly correlated for all the functional forms and distributions. The differences in the mean levels of efficiency are statistically significant across functional forms and distributions, although the magnitude of the difference is minuscule. The very high mean level of efficiency and the low standard deviation confirms that Québec dairy farms are very homogenous in terms of getting the most from their inputs. This is not surprising, given that the sector has been very stable policywise and that it has been difficult for dairy farmers to expand. To augment the comparisons, results obtained from data envelopment analysis (DEA), are added to the analysis. In this case, the correlation coefficients between DEA and parametric specifications are found to be very low.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".