Bibliographic record
Abstract
The export volume of Korean Ginseng products has been diminishing since 1990 because of their weakened international competitiveness. Nowadays, Korean Ginseng products are faced with lower price competitiveness and ineffectiveness of marketing strategies comparing with other Ginseng products from competing countries in the foreign markets. Furthermore, the superiority of processing technologies is being threatened by other developing countries like China and Canada. Thus, it is required to make a dramatic turning point for the export promotion of Korean Ginseng products. The objectives of this research are to examine the existing export strategies of Ginseng products, and to make some new export marketing strategies in order to promote the export of Korean Ginseng products. To achieve these objectives, ① the structures and trends of Ginseng markets of Japan and U.S.A. were analysed, ② questionnaire surveys on the foreigners` consumption of Ginseng products in each country were conducted, and finally, ③ market segmentation and making new export marketing strategies were implemented. In conclusion, the marketing basis for the export of Korean Ginseng products like appearance of Ginseng root, cultivation years, and the traditional fame have to be substituted for the emphasizing of functional differences, various products line-up, and modem senses. And it is required to develope the practices of consumer-oriented target marketing in accordance with the widespread selective purchasing behavior of foreign consumers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.033 | 0.009 |
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".