Discount Usage and Price Discrimination for Pork Products in Canada
Bibliographic record
Abstract
This paper examines the effects of demographic and location variables on usage of various types of discounts (e.g., coupons, price cuts, and store membership discounts) and compares the price elasticities of demand for pork products for users and nonusers of various types of discounts. We use Ipsos‐Reid's Consumer Panel of Canada weekly household data across Canada over a one‐year period. We find that the effect of demographic and location variables on discount usage is discount‐type specific. For many pork products, the price elasticity of demand is higher for users of discounts than for nonusers. The results also indicate variation across discount types in the ability to price discriminate for specific pork products. For example, coupons and price cuts are more effective price discrimination tools than store membership discounts for processed pork products. These results suggest that retailers should be strategic with the types of discounts used to most effectively price discriminate across a variety of pork products.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".