Fish and meat demand in Canada: Regional differences and weak separability
Bibliographic record
Abstract
The objective of this paper is to gauge to what extent there are regional differences in meat and fish demand across Canada. Three regions are defined for this purpose and the notorious problem of zeros in survey data is addressed by endogenizing the probability of purchase through the Shonkwiler and Yen approach applied to a QUAIDS demand system for each region. Probabilities of purchase and marginal effects are computed and compared as well as price and expenditure elasticities. The empirical distributions of the elasticities are simulated through bootstrapping. This allows us to formally test the null hypothesis of no regional differences between elasticities for central, western and Atlantic Canada and the null hypothesis of weak separability of fish from meats. While some significant regional differences were uncovered that can be exploited by food retailers, other findings were robust across regions. For example, the demand for fish tends to be more price and expenditure inelastic than the demand for meats. [EconLit citations: D120, C150, L660]. © 2006 Wiley Periodicals, Inc. Agribusiness 22: 175–199, 2006.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".