Optimal Generic Advertising with a Rationed Related Good: The Case of Canadian Beef and Chicken Markets
Bibliographic record
Abstract
An optimal advertising rule is derived for a good sold in an open market (beef) when a related substitute good (chicken) is production rationed and whose imports are subject to trade restrictions. Such a rule is developed using a multi‐market equilibrium displacement model that reflects demand interrelatedness, open trade of the advertised good (beef), with rationed production and restricted trade of the related good (chicken). The optimal rule nests earlier optimal advertising rules under a variety of conditions. Results underscore the importance of accounting for cross‐product advertising effects. When these effects are present (absent), the optimal generic beef advertising intensity in Canada is shown to fall (rise) with elimination of supply management in Canada's chicken sector. L'auteur dérive une règie sur l'optimisation de la publicité pour un produit vendu sur un marché libre (bœuf) en présence d'un produit de substitution rationné dont on restreint les importations. Pour parvenir à une telle règie, l'auteur a utilisé un modèle de déplacement du point d'équilibre sur un marché multiple illustrant les liens entre la demande des produits concernés, le libre‐échange du produit faisant l'objet de la publicité (bœuf) et la restriction de la production et des importations du produit apparenté (poulet). La règie d'optimisation englobe les règles antérieures sur l'optimisation de la publicité dans diverses situations. Les résultats soulignent qu'il est important de prendre en compte les retombées de la publicité sur les autres produits. Au Canada, en présence (absence) de telles retombées, le degré optimal de publicité générique sur le bœuf diminue (augmente) avec l'abolition de la gestion de l'offre de poulet.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".