Ethnic Niche Markets for Fresh Canadian Pork in the United States Pacific Northwest
Bibliographic record
Abstract
Summary The ethnic Asian market in Washington and Oregon constitutes a sizable niche market for fresh Canadian pork. The objectives of this study are to evaluate the Asian ethnic markets for fresh pork in the United States Pacific Northwest and Vancouver. Asian retailers and distributors in Vancouver, Seattle and Portland were surveyed by direct interview during November and December 1996. The survey applied semantic differential scaling questions, open-ended questions and a stated preference task, a conjoint methodology, to examine pork retailer's and distributor's perceptions of fresh pork produced in Western Canada and in the Midwest United States. The survey results show Western Canadian pork enjoys an image of superior quality amongst retailers and distributors in Seattle's ethnic Asian market. Asian retailers in Portland are less familiar with Western Canadian pork and did not regard it as highly as did retailers in Seattle. Distributors in Portland are more familiar with Western Canadian pork and consider it to be superior to Midwest United States pork in terms of overall quality, meat color and fat trim. In both markets, Western Canadian pork is generally considered to be expensive. These results are not statistically significant; however, they are of economic relevance since most of the major players in the segment were interviewed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".