Bibliographic record
Abstract
This paper explores the relationship between nation`s competitiveness and Olympic results of 2008 Beijing and 2010 Vancouver. We used economic level, population size, number of athletics, number of Olympic medals and path analysis to find out the relationships of these variables. We analyzed these data by correlation coefficient, regression analysis, and path analysis. We chose every nation which has at least one bronze medal as a unit of analysis. We found the following outcomes from the analysis of Beijing Olympic. First, the direct effect of population size on medals is positive but weak. Second, the indirect effect of population size on medals is positive and about twice larger than that of direct effect. Third, the direct effect of GDP on medals is negative and very weak. Fourth, the indirect effect of GDP on medals is positive and about ten times larger than that of direct effect. Fifth, the most effective path on medals is the number of athletics which come from population size and GDP. We also found the following outcomes from the analysis of Vancouver Olympic. First, the direct effect of population size on medals is statistically not important. Second, the indirect effect of population size on medals is positive and about 2.3 times larger than that of direct effect. Third, the direct effect of GDP on medals is positive and statistically significant. Fourth, the indirect effect of GDP on medals is positive and 2.4 times larger than that of direct effect. Fifth, the most effective path on medals is the number of athletics which come from GDP. We concluded that population is important variable in summer Beijing Olympic and that GDP is important variable in winter Vancouver Olympic.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".