Cost Structure of the Ontario Dairy Industry Revisited: Distributional Aspects
Bibliographic record
Abstract
The initially stated objective of the Canadian dairy supply management–farm revenue risk reduction–has been met well by the program. However, it is less clear whether the program has served all farms equally well. Namely, it is not known how successful the program was in enhancing the cost-effectiveness of smaller farms. This paper uses the 2006 Ontario dairy farm-level accounting data to compare the estimated cost structure with that identified by Moschini (1988). Next, farm size and profit distribution changes are assessed. Finally, the paper provides a simple framework for examining the relationship between current farm size and quota purchases by individual farms. The results suggest that the general cost pattern identified in the early 1980s has been retained. Average cost declines as output increases at lower output levels. However, the minimum-cost farm size has increased about threefold. Additionally, both output and profit distributions have become more skewed, with a lesser contribution by smaller farms. There is evidence that the possibility of quota exchange facilitated the greater expansion of larger farms and that the process of divergence in size and profit between small and large farms is continuing. These results have bearing on the sustainability of smaller farms.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".