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Record W1525409829

A comparative study of Olympic athlete cultivation systems in the People's Republic of China and the United States of America.

2005· book· en· W1525409829 on OpenAlexaboutno aff
Tan Zhang

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2005
Typebook
Languageen
FieldMedicine
TopicWinter Sports Injuries and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsChinaPolitical sciencePeople's RepublicGeographyLaw
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score0.868

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.251
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2005
Admission routes1
Has abstractyes

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