Optimal Collective Investment in Generic Advertising, Export Market Promotion and Cost‐of‐Production‐Reducing Research
Bibliographic record
Abstract
Optimal investment rules are developed for a producer agency investing in domestic‐market generic advertising, export market promotion, and cost‐of‐production‐reducing research. These rules are derived assuming either maximization of producers' surplus or social surplus. The form of the optimality rules differs according to which objective is pursued. Fixed producer agency budgets are also allowed by incorporating a constraint limiting total expenditure on the three activities. Addition of such a constraint substantially alters the structure of the optimal investment rules. Differences in these rules highlight the importance of accounting for the financing mechanism when modeling optimal checkoff fund investment decisions. Optimality rules are simulated using data for the Canadian beef sector. Results suggest historic underinvestment in domestic‐market generic advertising but overinvestment in export market promotion. Sensitivity of simulation results underscores the difficulty in assessing optimality of historic producer investment in cost‐of‐production‐reducing research.
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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 | 0.004 | 0.013 |
| 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 | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".