The Demand for Pork Products in Canada: Discount Promotions and Cannibalization
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT We estimate the effectiveness of offering discounts on the demand for featured pork products using Ipsos‐Reid's Consumer Panel of Canada data for a 1‐year period from April 2007 to March 2008, and explore whether there are any positive or negative spillover effects on non‐featured products because of these discounts. We find strong evidence that, as expected, discounts increase the purchases of the featured products. However, we find that offering discounts may have cannibalistic effects – negative cross‐discount effects – on non‐featured products, and that these effects vary considerably among pork products. From the retailers’ perspective, as profit margins have become smaller, our results indicate the need to develop a promotion strategy that minimizes the cannibalistic effects from discounts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it