Production Rigidity, Input Lumpiness, Efficiency, and the Technological Hurdle of Quebec Dairy Farms
Bibliographic record
Abstract
Abstract In this paper, we argue that (bilateral) auctions of production quotas induced a rapid convergence in dairy farm size within provinces in the early years of Canada's supply management policy and that this effect was stronger in provinces with a larger number of dairy farms. This contributed to the smallness and homogeneity of Quebec dairy farms relative to dairy farms in Western Canada. In Quebec, most dairy farms still rely on the tie‐stall milking system, while dairy farms in Western provinces are larger and use larger‐scale, lower‐cost technologies. Regulations on Quebec's quota exchange have slowed down the pace at which a farm can acquire production quota, exacerbating the effects of input lumpiness, on dairy farm efficiency. Low trading on the production exchange severely constrains production adjustments, making, scale, allocative, and technical inefficiencies more persistent and investment in herd expansion unprofitable.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".