Rendezvous Upon Ice: How Canada Is Aiding In The Development Of Hockey In China Ahead Of The 2022 Winter Olympic Games
Bibliographic record
Abstract
Since the awarding of the 2022 Winter Olympic Games to Beijing in 2015, China has seen tremendous growth in the development of ice hockey in several regions of the nation. Being awarded the Olympic Games has often become a catalyst for the development of sports in many parts of the world, especially those areas which host the games. Since hockey in China is still underdeveloped and the nation is still somewhat new to this globalizing sport, they sought out the expertise of Canadians, who live in a country which is home to a rich hockey culture and impressive international hockey record in order to help them rapidly develop the sport. This research project seeks to understand the role Canadian actors are playing in the development of Chinese players both in Canada and in China using Global Production Network (GPN) theory to understand this phenomenon. The mutual cooperation between these two countries in regard to the development of hockey is hoped to lead the overall value enhancement of the sport in China. In addition, this project will also investigate how knowledge is transferred between Canadian and Chinese actors within this network using Knowledge Management theory in order to help us understand what knowledge transfer processes are at play as well as the strengths and weakness of knowledge transfer among actors. Finally, this project will investigate the globalization of hockey and how specifically it has globalized and spread to China in order to help us understand how the sport has developed in the nation over time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".