Consumer preference for production‐derived quality: analyzing perceptions of premium chicken production methods
Bibliographic record
Abstract
Abstract The authors assess consumer interest in a food product containing production‐derived attributes. They use the French Label Rouge system in the Ontario chicken market as an example of a producer‐controlled quality system. Conjoint analysis reveals a significant proportion of respondents value nonprice attributes; medication and housing had the highest importance scores, followed by price and brand ownership. Cluster analysis of the part‐worth utilities revealed three consumer segments: price conscious consumers; consumers focused on naturalness; and those focused on animal health. Segments do not appear to differ on the basis of socioeconomic and demographic profile of respondents. However, multiitem scales reflecting attitudes towards production systems vary significantly across segments. Price‐conscious respondents show agreement with use of medication and express concern over quality. Respondents in the naturalness segment express concern over quality, locality of production and impact of production methods on own health. Animal‐health‐conscious respondents show agreement with the use of medications, concern over quality, locality and impact of production methods on own health, but neutrality towards byproducts and traditional production methods. [EconLit citations: D120, Q130]. © 2009 Wiley Periodicals, Inc.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".