Ridge Regression Analysis on the Influential Factors of FDI in Jiangsu Province
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Bibliographic record
Abstract
As Chinese eastern coastal developed areas, through the use of foreign capital, Jiangsu Province has not only promoted economic growth rapidly, enhanced the regional comprehensive competitiveness, promoted employment, but also created a new famous mode of economic development called Sunan. Based on the qualitative analysis of factors affecting the inflow of foreign capital in Jiangsu, the paper establish a mathematical model between the FDI and major economic indicators in Jiangsu, in accordance with its own characteristics. And then taken 1992-2006 time-series data for the background, the paper use the method of ridge regression to analysis the influential factors of FDI in Jiangsu. Key words: foreign direct investment, ridge regression, factors, Jiangsu Resume: En tant qu’une region developpee dans la cote-est de la Chine, grâce a l’usage du capital etranger, la province du Jiangsu a non seulement eu une croissance economique rapide, augmente la competitivite generale, cree desemplois mais aussi invente un nouveau modele du developpement economique qu’on appelle Sunan. En se basant sur les analyses qualitatives des facteurs affectant l’afflux du capital etranger dans la province de Jiangsu, l’article etalit un modele mathematiqueentre le FDI et les principaux indicateurs economiques dans la Province, conformement a ses caracteristiques appropriees. Et puis, en employant les donnees de la periode de l’annee 1992 a 2006 comme l’arriere-plan, l’article utilise la methode d’analyse de ridge regressionn pour etudier les facteurs influents de FDI dans la province de Jiangsu. Mots-Cles: investissements directs etrangers, ridge regression, facteurs, Jiangsu
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it