Bibliographic record
Abstract
This research compares and analyzes cases of Korea, England, America, and Canada College & University curriculum of golf department and subsequently inquire into means to improve Korea University curriculum of golf department. For this purpose Konkuk University, Kyunghee University, Yongin University, Semyung University, Chung-ang University, Joongbu University, Hoseo University of Korea, Guildford College, Hartpury College of England, PGCC, Arizona State University, Stanton University, Pennsylvania State University, Ferris State University of America, Holland College, Lethbridge Community College, Humber College, Camosun College of Canada have been selected. In order to determine ways to improve Korean educational programs, the analysis was based on the aims and curriculums of each golf university. This research introduces the following method to improve curriculums of golf department in Korea University. First, each university must establish a clear and specific goal. Specialized education goals must be diversified even within degrees must be issued thus expanding the field of education systematically. Second, curriculum in which other departments and universities are connected should be builded so that golf education may be conducted systematically and or professionally leading to consistent education in golf. Third, various specialized programs in which internal/external golf industry are involved should be established to expand the field of golf education. This subject shall provide an opportunity for students to utilize their acquired knowledge, and get a job in various fields. Such methods shall construct an advanced education system and lead to a specialized and differentiated golf education. Moreover, such golf education shall promote students with professional talent contributing to establish an enhancement on golf education.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".