An Investigation of the Marketing of Butterfat by the Canadian Dairy Industry
Bibliographic record
Abstract
This study examines the Canadian Dairy Commission’s marketing of butterfat. Previous studies have concentrated on the evaluation of butterfat by using total kilograms of milk. Measuring milk as kilograms is based the assumption of fixed proportions between kilograms of milk and kilograms of butterfat. However, measuring dairy using kilograms may not be a good proxy for the underlying butterfat. In this study we argue that dairy fat maybe an inferior factor of production, whereas kilograms is a normal factor of production. This means that following kilograms within the marketing system may not track butterfat. In fact, butterfat may respond in an opposite direction to kilograms when prices and incomes change. Assuming that butterfat is an inferior factor may explain some of the marketing practices of the provincial marketing boards that on the surface seems to be neither in the interest of consumers or dairy farmers. If the objective of the supply management is to make dairy producers better off, then basing dairy quota on kilograms of butterfat seems logical since the demand for butterfat has been rising over time. In addition, controlling supply at the retail level using minimum milk price supports also benefits producers, although it may not be in the best interest of consumers due to higher dairy prices and increased butterfat consumption.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 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) | 0.005 | 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".