Study on Economic Factor Relation of Jiangsu Counties and Evolution Process Based on Quantile Regression
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Bibliographic record
Abstract
For the problems which the assumption of strong conditions in using OLS to estimate parameters in regression models and dilemma in series testing,This paper introduces the nonparametric quantile regression to construct elements relationship models,and takes a case of Jiangsu counties economic development during 2000-2010to analysis.The results show that:1)Compared with OLS,QR fitting results for the counties economics overall simulation effects and the abilities of describing evolution character is better.2)According to the variables relationship structure of QR,we can divide the driving mechanisms into three different types:industrial structure dominant,general equilibrium driving and efficient equilibrium driving.3)The regions have structural changes in evolution process in Suzhou-Wuxi-Changzhou,the quantile points of every county′s evolution process transition affected by the economic factors waved during the periods and driving mechanism changed,and the counties developing path shows diversification.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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