A Research on Supportive Policy for Domestic Winter Sports on the Occasion of 2018 Pyeongchang Winter Olympics
Bibliographic record
Abstract
In Durban July 2011 Pyeongchang in Gangwon-do Province was chosen to be a host of the 23rd Winter Olympic Games. Thus, Korea has become one of countries that have held the four biggest sports events including Summer Olympic Games, the World Cup and World Championships in Athletics in the world for the last thirty years. Above all, the hosting 2018 Pyeongchang Winter Olympics has provided very important opportunities to enforce the national competitiveness internationally and revitalize domestic winter sports nationally. The third challenge succeeded thanks to the diverse endeavors so far to host the Pyeongchang Winter Olympics. Meanwhile, facilities and infrastructure have been built up, human power and organization have been developed and strengthened, supportive policies of the Government and related institutions have been established, winter-related sports industries have been promoted, and other various contents have been developed. Accordingly, Korea placed the fifth overall in the 2010 Vancouver Olympic Games, and sports stars appeared such as Kim Yuna, Mo Tae-Bum, Lee Sang-Hwa, Yi Seung-Hun and etc. The young people from snowless countries have been invited to Korea through the Dream Program. The number of young players of figure skating has been increased a lot. This way, environments related to winter sports have been transformed in various ways. Particularly, ‘Drive the Dream II’ was announced as a vital measure of the Government to host the 2018 Pyeongchang Winter Olympics successfully, so that the foundation on which Korea becomes a developed country of sports has been established. Thus, this study purposes to contribute to the successful hosting of the 23rd Winter Olympic Games which is going to be held three years later by considering the current situation of winter sports of Korea.Keywords: Domestic Winter Sports, 2018 Pyenogchang Winter Olympics, Supportive Policy
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".