Bibliographic record
Abstract
China’s rise in recent years to become an economic superpower has been accompanied by active policies to promote sport and culture. Once chosen by the Olympic Committee in 2015 to host the 2022 Winter Olympics, China faced a problem since its hockey team was not strong and could potentially be humiliated in the climax events of the Games. The Chinese Ice Hockey Association (CIHA) decided on a strategic coupling with Canadian hockey expertise to create credible men’s and women’s hockey teams, including hiring Canadian coaches, taking teams to Canada for extended professional training, playing exhibition games against Canadian teams, inviting Canadian hockey teams and players to China, and recruiting Canadian hockey players with Chinese heritage to play professionally in China on the HC Kunlun Red Star team (men) and the KRS Vanke Rays (women). We label this relationship a global player production network. However, the strict Covid lockdown in China and the froideur that developed in Canada-China geopolitical relations after the detention in Vancouver of Meng Wanzhou and the arrest in China of Michael Spavor and Michael Kovrig, was followed by a decoupling of the Canada-China hockey relationship. But by granting national status to Canadian-born “heritage” players, and by selecting at the eleventh hour the Kunlun Red Star and Vanke Ray teams as their Olympic teams, China effectively re-coupled with Canada and was thereby able to field two credible hockey teams.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.053 | 0.007 |
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".