Consumers Preferences Analysis Toward International Marketing Strategy for Salmon from Japan
Bibliographic record
Abstract
In this paper, we construct an international marketing strategy aimed at expanding salmon exports from Hokkaido, Japan. We conduct online surveys with consumers from the United Kingdom (UK), Singapore, and South Korea, which are among the priority countries / regions in the “Hokkaido food export expansion strategy.” We carry out a binomial logistic regression analysis using the factors that determine consumers’ food preferences and how consumers differentiate between producing countries. As a result, we find that the factors that increase the probability of consuming salmon include “consumers believing that a country's fish is wild-caught,” “consumers believing that a country's fishing is sustainable,” “consumers being young” (the UK and South Korea only), “consumers having high-frequency seafood consumption “ (the UK only), and “consumers having a high income” (the UK only). We also find that the UK does not differentiate between foreign salmon-exporting countries, while in Singapore, Japan’s salmon is valued higher than fish from countries such as Chile, Russia, and Canada. In South Korea, on the other hand, Norway, the United States (Alaska), and Canada ranked the highest, while Japan ranked the lowest.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".