Bibliographic record
Abstract
Beauty schools in Korea have traditionally focused on educating students to pursue careers mainly in hair designer, skincare esthetician, make-up artist. However, there is pressing need for professionals who possess knowledge and know-how in such area as herbal consulting, herbal research, herbal manufacture and herbal formulating. Here in Korea, the concept of herbalism is in the stage of introduction, compared with developed countries such as USA, Canada and Australia. Herbalist education program, which have been successfully implemented in U.S and Australia is recently drawing attention in Korea. The objective of this study is to suggest the fundamental data in order to propose the future direction of herbalist education programs in Korea. This study first examines a trend toward increasing demand in the domestic and global well-being industry. Then, several representative herbalist programs in college and university in the U.S and Australia are investigated including curriculum, application requirements and tuition fee. This study started based on the idea that under the circumstance the market for herb is on expansion, education programs for pre- herbalist and understanding of the market should be preceded. Therefore, Korea should exert itself to internationalization, encouraging participation in international herbal science activities and a lot of professional education programs suitable for Korean circumstance and characteristics should be carried out. We discuss potential possibility and a variety of issues to be considered in order to successfully implement herbalist education programs in Korea.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".