The Effect of Crude Oil Price to the Economic Growth of China:A Comparative Analysis between China and G7
Bibliographic record
Abstract
Using panel data and VAR etc,the paper compares the effect of crude oil price to China with G7,and observes that 1.it’s different that the crude oil price to Chinese economic growth and to other western countries.There are three country,Japan,America,and Canada,which Index of GDP is affected by the synchronous crude oil price,and China is not included.2.Index of Chinese GDP is affected by synchronous Index of America GDP and English GDP.3.Index of Chinese GDP is not affected by lag Index of G7 GDP,but Index of most western country GDP is affected by lag Index of Chinese GDP.4.Crude oil price is affected by Index of Chinese GDP.The paper suggests that 1.Japanese energy policy should be studied by China.2.America and English economic mechanism about how to find the cheaper price in the world wide should be studied by China when China import more raw material or other commodity in the future.3.China should improve RMB international status on the solid ground of comprehensive national strength.4.China should reduce the expenditure of energy,and improve internal energy price is an effective method.Besides the paper discovers that China get the limited and short-term economic profit when she deals with western countries and this is a question for China to solve.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".