Bibliographic record
Abstract
Real business cycle (RBC) is an important concept fortheoretical economic research. And it works well in U.Ssituation. In this paper, we use data from China in theclassic RBC model. Firstly we restate this model in detail.Then we apply this model by using data from China, andset specific parameters appropriate for China. From theresult, we find investment more volatile in China than inthe U.S, and investment is more relative to GDP growth,which is consistent with the fact we know about economicgrowth in China. Key words: Real Business Cycle; China’s Economy;GDP; HP Filter Resume Le cycle du Business reel (RBC) est un concept importantpour les theories de la recherche economique. Et ilfonctionne bien en situation U.S. Dans ce papier, nousutilisons les donnees en provenance de Chine dans lemodele classique de RBC. Tout d’abord nous rappeler cemodele en detail. Ensuite, nous appliquons ce modele enutilisant des donnees en provenance de Chine, et definirles parametres specifiques appropriees pour la Chine.D’apres le resultat, nous trouvons l’investissement plusvolatile en Chine qu’aux Etats-Unis, et de l’investissementest plus par rapport a la croissance du PIB, ce qui estcoherent avec le fait que nous savons sur la croissanceeconomique en Chine. Mots-cles: Cycle du Business reel; Economie de laChine; le PIB; HP Filter
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".