Evaluation on the Effect of Regional Development Policy: The Case of Guizhou Province’s Catching-up Strategy in China
Bibliographic record
Abstract
China has been facing the dangerous dilemma of unbalanced regional development as the world does. With the coming of the two centenary goals, facing the peculiar difficulties and present conditions in Guizhou province, the central government of the People’s Republic of China made and implemented the catching-up strategy in Guizhou province in 2012. This paper regards implementing catching-up strategy in Guizhou province as a social quasi experiment, chooses 15 middle and western provinces or municipalities to compose control group, applies provincial panel data from 1998 to 2017, and uses synthetic control method to acquire a synthetic Guizhou province which is specified as a counterfactual condition of Guizhou after 2011 to study the economic effects of catching-up strategy quantitatively. The conclusion of positive econometric analysis indicates: from 2011 onward when implementing catching-up strategy, Guizhou Province’s growth rate of real GDP is higher than ‘the synthetic Guizhou’ by 1.4 percent to 3.4 percent. The paper asserts that in comparison with the universal strategy of regional development, practicing targeted catching-up strategy aiming at special region could realize surpassing speedily.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".