Dynamic Relationship of Industrial Structure Change and Economic Fluctuations: Evidence from Sichuan, China
Bibliographic record
Abstract
Industrial Structure Change is not only an important source of economic growth; it’s also an important driving force for economic fluctuations. In this paper, on the basis of combing the literature, and the relevant data in 1978-2013 of Sichuan Province in China, and the use of empirical VAR model to analyzes the mutual dynamic influence of Sichuan Industrial Structure Change and economic fluctuations. The study found that the rationalization and optimization of the industrial structure both impact on economic fluctuations, but the impact are on the opposite direction. In the short term, the industrial structure rationalization and industrial structure optimization respectively has positive and negative effects on economic fluctuations; in the long term the opposite. Industry Structure optimization fluctuations shows a great influence on economic fluctuations, while the rationalization of the industrial structure shows a relatively small negative effect. The impact of economic fluctuations on the rationalization of the industrial structure fluctuation and optimization performance for the negative and positive relationships. d annual reports of the sampled firms and their market values obtained from the official daily list of the Nigerian Stock Exchange (NSE) over a period of 10 years (2001-2010). Using multivariate regression as technique for data analysis, the study established that accounting information of Food & Beverages companies in Nigeria is value relevant. Accordingly, the study recommends the use of financial statements figures of Food and Beverages firms for investment decision.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".