MétaCan
Menu
Back to cohort
Record W1974128919 · doi:10.1300/j047v10n04_03

Ethnic Niche Markets for Fresh Canadian Pork in the United States Pacific Northwest

2000· article· en· W1974128919 on OpenAlexaffabout
Peter Kuperis, Michel Vincent, James R. Unterschultz, Michele M. Veeman

Bibliographic record

VenueJournal of International Food & Agribusiness Marketing · 2000
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWine Industry and Tourism
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEthnic groupChinaGeographyConjoint analysisBusinessAgricultural economicsSocioeconomicsPreferencePolitical scienceEconomicsArchaeology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.019
GPT teacher head0.237
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2000
Admission routes2
Has abstractyes

Explore more

Same venueJournal of International Food & Agribusiness MarketingSame topicWine Industry and TourismFrench-language works237,207