Modeling the demand for alcoholic beverages and advertising specifications
Bibliographic record
Abstract
Abstract In this paper, the demand for beer, wine, spirits and soft drinks in Ontario is modeled in two parts: an equation is specifiec to endogenize group expenditures and a demand system is set up to allocate budgeted group expenditures across types o beverages. Advertising is allowed to influence both the level of group expenditures and its allocation. Three popula advertising specifications are compared using theJ‐test and the likelihood dominance criterion. Even though all threi specifications fitted well according to standard criteria, the calculated expenditure, price and advertising elasticities wen sensitive to the manner with which advertising is specified. This clearly highlights the need to rely on a sound criterion t< identify a dominant specification. From the identified dominant specification, we found that advertising has very subtle effect on expenditures on alcoholic beverages (group and individual beverages). Thus, advertising is not effective in enlarginj markets and this suggests that firms (especially breweries) use advertising to compete in zero‐sum market share games. From i public policy perspective, our results are comforting but future research should investigate whether the neutral effect o advertising on aggregated expenditures hide substantial offsetting changes in the drinking habits of individuals.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".