Economic Evaluation Models of Generic Fluid Milk and Cheese Marketing Investment in Canada for the 2007-2011 Period
Bibliographic record
Abstract
The Dairy Farmers of Canada (DFC), working on behalf of dairy producers, invest considerable sums of money in marketing activities for various dairy products. While previous studies (recently by Kaiser, Cranfield, and Doyon, 2006, 2007 and 2010) suggest that investment in generic fluid milk and cheese advertising and promotion does, indeed, generate a positive net return, sufficient time has lapsed to warrant a re-evaluation of these efforts. Moreover, the availability of a new source of data allows to a different type of modeling and the inclusion of more detailed household data. This study estimates demand systems for Ontario and the Maritimes using data from A.C. Nielsen (i.e. Homescan) and data provided by DFC. It also estimates a demand systems for cheese in Canada (without Quebec) also using data from A.C. Nielsen (i.e. Homescan) and data provided by DFC. The Almost Ideal Demand System (AIDS), which allows estimated elasticities to vary over time, will be used in the econometric analysis. Based on these demand systems, simulation are used to undertake the calculation of return on investment.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".