Bibliographic record
Abstract
This study investigates coaching degree courses and non-coaching degree courses at domestic and foreign universities for academic establishment and continued growth of coaching. To this end, we collected, compared, and analyzed coaching curriculums and operating methods of domestic and foreign universities through RISS, site search, literature research, academic associations and international coaching associations.The results of this study are as follows. Coaching degree programs were established at 11 universities in Korea, and various education courses were established for non-coaching degrees at 6 universities. Foreign universities were offered in 8 universities, including 2 in the United States, 1 in Canada, 2 in the United Kingdom, 1 in Ireland, and 2 in Australia. Non-degree coaching courses were held at 19 universities including 11 in the United States, 2 in Canada, 3 in the United Kingdom, 1 in Ireland, and 2 in Australia. In addition, 5 universities in Korea and 6 foreign universities operated the International Coaching Federation(ICF) Certification Program.As such, the spread of a coaching curriculum in domestic and foreign universities systematizes coaching into academics. As the efforts to increase the capacity of coaches have been actively developed around the academic world, the need for diversified coaching research in the academic world has been significantly increased for the growth of Korean coaching studies.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".