A comparative study of Olympic athlete cultivation systems in the People's Republic of China and the United States of America.
Bibliographic record
Abstract
Historical and sociological studies have revealed the disparities of sport among different countries. This study uses Bereday's (1964) model for comparative education to explore the differences in the Olympic athlete cultivation between the People's Republic of China and the United States of America. Treating the process of athlete cultivation as a dynamic system, this study offers general descriptions of athlete cultivation in both countries; evaluates the backgrounds of the two countries from historical, political, economic, social, and cultural perspectives; identifies the most significant components of athlete cultivation; and finally explores the similarities and differences. The discussion focuses on the studies of backgrounds, which are the causes of the current differences. Particularly, the study gives attention to the juxtaposition of a set of components that are shared by both countries related to their athlete cultivation. In all, the comparison identifies the differences that are determined by the respective background of each country and the tendency to adopt a similar cultivation system under the influence of globalization. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .Z535. Source: Masters Abstracts International, Volume: 44-01, page: 0063. Thesis (M.H.K.)--University of Windsor (Canada), 2005.
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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 | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".