Assessing the demand for value‐based organic meats in Canada: a combined retail and household scanner‐data approach
Bibliographic record
Abstract
Abstract We apply sets of weekly retail and household scanner data to estimate consumer demand of selected organic and conventional fresh beef products in the Canadian retail market. The main contribution of our study stems from the application of a two‐stage procedure that provides new and deeper insight into consumers' responses to changing retail environment and pricing for organic and conventional meat products. Combined knowledge of point‐of‐sale consumer behaviour for value‐based products, such as organic products, and distinct socio‐demographic profiles of buyers vs. non‐buyers of meat is especially interesting for retail managers and meat industry stakeholders. First, household meat consumption patterns are investigated based on household scanner data that track household's meat purchases in the period 2006–2007. The second step of analysis then involves the estimation of an almost ideal demand system for selected organic and conventional fresh beef products using retail scanner data for the period 2000–2007. The introduction of greater selections in organic product lines across mainstream supermarkets in Canada in response to consumer health concerns is expected to spur retail competition in an otherwise saturated Canadian retail market. The analysis of socio‐demographic profiles in beef consumption using individual household's purchase data reveals that besides regional differences in preferences, household size and resource characteristics are major determinants of point‐of‐sale beef purchase decisions. Our demand system results indicate that organic beef is highly dependent on price and expenditures, whereas demand for conventional beef is mostly driven by income, habits and ‘typical’ Canadian seasonal beef consumption patterns. Altogether, our conclusions on organic beef vs. conventional beef buyers may have further implications for institutional regulations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".